32results about How to "Low time complexity" patented technology

The invention discloses a method for detecting and classifying all-network flow abnormity on line. The method comprises the following steps of: (I) acquiring network flow (NetFlow), namely, receiving a NetFlow data packet transmitted from a border router by adopting a NetFlow collector, resolving the data packet and aggregating data streams to form data suitable for statistical analysis, and transmitting the data to a central control board through network to store in a database; (II) building a flow matrix taking the entropy of flow characteristics as measure; (III) detecting the flow abnormity on line by adopting a main increment component analyzing method; and (IV) constructing sample points in four-dimensional space by utilizing residual vector acquired through on-line detection and classifying the flow abnormity on line by adopting an increment k-mean value clustering method. The method has the advantages of detecting the flow abnormity on line, classifying the flow abnormity on line in real time, meeting the requirement on the real-time detection and classification of the flow abnormity better and laying the technical foundation for subsequently defending against network attack, along with lower time complexity and storage expenditure.

The present invention discloses a quick comparing and positioning method for a gene sequence segment on a reference genome. The method comprises: firstly, extracting the gene sequence segments from the reference genome; aiming at each gene sequence segment, establishing a key value pair by using the gene sequence segment as a key part and using target information of the gene sequence segment as a Value part, mapping the gene sequence segment by adopting a Hash function to determine a target storage location in a database, and writing the key value pair into the target storage location to complete database establishment of the reference genome; and when quick comparison and positioning need to be carried out on a gene sequence segment to be matched, firstly, mapping the gene sequence segment to be matched by adopting the Hash function to determine a target storage location of the gene sequence segment to be matched in the database, and then reading the target information of the gene sequence segment of the key value pair corresponding to the matched gene sequence segment from the target storage location. The quick comparing and positioning method disclosed by the present invention has the advantages of low time complexity, high comparing and positioning speed, high positioning efficiency, wide application range, and applicability to cross-species hybrid quick analysis.

The invention discloses a classifier construction method based on active learning. The current value and the prospective value of unlabeled samples are fully considered, so that samples with high value are selected. The method includes the steps that first, the current value of samples is calculated by using the optimal class information and the suboptimal class information of the samples first, and a part of samples with high value are selected to constitute a candidate sample set according to the high-low current value of the samples; afterwards, the prospective value of the samples in the candidate sample set is calculated, and the total value of the samples is obtained combined with the current value of the samples; finally, unlabeled samples with high value are selected to be labeled according to the total value of the samples, the unlabeled samples are added into a training sample set, and a classifier is updated. The experimental result on the basis of different data sets shows that the classifier with high classification accuracy can be obtained through the method under the condition that the samples of the same quantity are selected.

The invention belongs to the technical field of agriculture, in particular relating to a DSP (Digital Signal Processor)-based rice weed recognition system and method. The DSP-based rice weed recognition system consists of an image acquiring module, a rice weed recognition module and an image displaying module, wherein the image acquiring module and the image displaying module are connected with the rice weed recognition module respectively by virtue of an I2C (inter-integrated circuit) bus and a BNC (Bayonet Nut Connector) data bus; the image acquiring module consists of a CCD (charge-coupled device) camera supporting high-definition image and an SAA7115 decoding chip of Philips, the image displaying module consists of a Gadmei PL7006 liquid crystal television used for displaying a television signal, and the rice weed recognition module consists of a DM642DSP image processing chip, and expanding SDRAM (synchronous dynamic random access memory) and SAA7121 (system application architecture 7121) decoding chips. According to the invention, the real-time recognition of weeds in rice fields can be realized, and the system miniaturization and portability can lead the system to be capable of being flexibly arranged on rice planting field equipment, and improving the practical application range of the system.

The invention discloses a method for identifying and processing abnormal indexes of distribution network based on large data. The method comprises the following steps: collecting real-time data of special bus transformer operation of distribution network and sending the real-time data to an intelligent operation and maintenance management and control system of distribution network; storing the real-time data in a distributed database HBase; the SPARK being used to load the running real-time data from the HBase database into the memory, the outlier of the running data being identified and deleted by the iForest algorithm, and the remaining data subset being deleted by k-Means algorithm clustering; after clustering, the average value at the corresponding dimension of each category is used tofill the deleted outliers. The device adopted by the method comprises a data acquisition module, a loading module, a culling module, a clustering module and a processing module. The invention utilizes the distributed database HBASE to store the real-time data of the dedicated metering operation, and timely finds out the defects of the real-time data of the distribution network operation and makescorrections through the efficient analysis of the massive data by the distributed and parallel computing framework SPARK of the big data.

The invention discloses a text retrieval method and a relevant device. The method comprises: Input primary text data; perform self-adapting mapping process with a method of descent for the primary text data; carry forth retrieval text data similar to the data after the self-adapting mapping process with the method of descent; output the retrieval text data. The invention execution example can fulfill effective compression for original data after decency for higher-dimensional text data; the invention is characterized in rather low time complexity, adapting to magnanimity data, and effectively maintaining similarity between all vectors of the text data. Utilization of the invention execution example method or the device can fulfill quick response to such requests as network text inquiry, search, retrieval and etc at rather low arithmetic cost, so as to resolve the problems in prior network text retrievals of high time complexity, large space consumption and rather low accuracy.

The invention belongs to the technical field of remote sensing image processing and particularly relates to a hyper-spectral remote sensing image mixed pixel decomposition method based on a distance geometry theory. The invention provides an operational formula for calculating an areal coordinate of a high-dimensional data space by introducing the distance geometry theory into a hyper-spectral remote sensing image mixed pixel according to physical characteristics of a hyper-spectral image and geometric characteristics of a data set, and obtains a position estimation algorithm which can well keep a geometric structure of the data set according to a distance geometric constraint; and finally, a novel high-precision and low-complexity abundance estimation algorithm, namely the abundance estimation algorithm based on a distance geometry, is obtained. The algorithm has good applicability to various different hyper-spectral data (including emulated data and actual data sets). The hyper-spectral remote sensing image mixed pixel decomposition method based on the distance geometry theory, disclosed by the invention, has very important application value on aspects of high-precision ground feature classification of multispectral and hyper-spectral remote sensing images, and detection and identification of a ground target.

The invention discloses a method for quickly searching for multiple shortest paths, which belongs to the field of computer networks and solves the problem of calculating multiple (assumed to be K) shortest paths from a source node to each other node. In the prior art, classic methods such as Dijkstra and A* algorithms are used, which require multiple iterations and complicated calculations; at the same time, the calculated paths cannot effectively avoid loops, and the actual use value is low. The method proposed by the invention can obtain the K shortest paths from the source node to all other nodes at one time, and there is no loop. It is assumed that the water flow starts from the source node and flows at a constant speed, and starts to diverge after reaching a confluence point. Record the diversion information at the confluence point to obtain a shortest path from the source node to the confluence point. The confluence point records K pieces of diversion information and then stops the diversion; proceed in this way, when all confluence points record K pieces of diversion information, you can get The shortest K paths from the source node to all the sink points. The present invention can be used in logistics scheduling, GPS navigation and other practical applications.

The invention discloses a picture geographic location method based on large-scale street view data and a system for realizing the method. The method comprises a real-time matching part, namely the following steps: extracting local feature points from a queried picture as query feature points, and generating a query feature set; in an index structure, searching K nearest neighbor feature points foreach query feature point, and generating a nearest neighbor feature distribution table; obtaining a nearest neighbor feature set of a candidate picture by virtue of the nearest neighbor feature distribution table; and selecting a candidate picture corresponding to an optimal feature set in the nearest neighbor feature set as an optimal matched picture, wherein a geographic location correspondingto the matched picture is regarded as the geographic location of the queried picture, and K is an integer. The method also can comprise a preprocessing part preferably, namely a time-consuming 'indexstructure creating part' which is separately processed in advance. The method and system which are disclosed by the invention has the characteristics that location accuracy is high, matching speed ishigh and globally optimal solution can be found.

The invention discloses an image super-resolution reconstruction method based on cascading linear regression. The image super-resolution reconstruction method mainly solves the problems that an existing method is unstable in reconstruction process and low in efficiency and too much pseudomorphism exists in generated high resolution images. The realization process comprises the following steps: (1) constructing a training image set; (2) learning training images to determine a T-group linear regression device and a T-group clustering center; (3) carrying out preprocessing on tested low resolution images to obtain initial estimated high resolution images, and extracting different components of the images; (4) blocking the brightness component, and carrying out initial estimation on the brightness characteristic block; (5) carrying out iterative updating and reconstruction on the initial estimated characteristic block to obtain high resolution image blocks; (6) combining the high resolution image blocks to obtain a high resolution brightness component image; (7) splicing the high resolution brightness component image and the chromaticity component to obtain high resolution images. The generation of pseudomorphism is reduced, the definition of the reconstructed images is improved, and the method can be used for high definition video display.

The present invention provides a kind of calculating method that is applicable to the critical polygon of adjoining in between any two polygons, at first, this method is by judging each corner vertex (vector side) of polygon and each vector side (corner vertex) of another polygon According to the contact situation, the initial set of vector line segments is calculated; then, the intersection points of the initial vector line segments are used to divide the vector line segments; finally, the "minimum rotation angle" strategy is used to screen the appropriate vector line segments to synthesize the inner critical polygons (including special degenerate point and degenerate line), that is, the inner abutment critical polygon (set) between these two polygons. The invention calculates the inward critical polygon between two polygons with arbitrary shapes through the vector line segment synthesis method, which can not only reduce the time complexity of calculating the inward critical polygon, but also improve the accuracy of the calculation method of the inward critical polygon and scope of application.

The invention discloses a large-scale complex network community discovery method based on local neighbor information, and the method comprises the following steps of A, obtaining a network structure,and selecting an unvisited node with the maximum selection degree and all neighbor nodes of the unvisited node as an initial community; B, deleting the nodes which are not tightly connected in the community; C, selecting an alternative node to expand the initial community by utilizing the local information of a network; D, calculating a node membership coefficient to judge whether the node shouldbe left, obtaining a community, and marking all nodes of the community as accessed nodes; and returning to the step A until no unvisited node exists, and outputting a community division result. The invention further provides a computer virus propagation control method which comprises the steps of constructing a computer network model, using the above method for obtaining and dividing the communities, and mainly protecting the community core nodes and the boundary nodes. The method has the advantages that the method has the good calculation efficiency and algorithm precision on the super-large-scale problem.

The invention relates to an automatic meter searching method of an ammeter acquisition terminal, which comprises the following steps of: dividing all meter address ranges to be searched into two sections to realize halving processing, respectively sending meter address query messages corresponding to an upper limit value and a lower limit value of each section of meter address to each section of segmented meter address, if the ammeter corresponding to one section of meter address range does not return the message information, stopping the meter searching work, if a meter address to be queriedand a legal message returned by the meter exist in the meters corresponding to a section of meter address range, by a meter acquisition terminal, recording ammeter information corresponding to the meter address and stopping meter searching work, if the ammeter corresponding to one section of the meter address range has the meter address, to be queried, returned by the ammeter, and if the returnedmessage is illegal, by the ammeter acquisition terminal, performing half-folding processing on the segment of the meter address range, and stopping the meter searching work until the state of finishing the meter searching work is reached, thereby accelerating the meter searching speed of the ammeter acquisition terminal and improving the meter searching efficiency.

The invention provides a micro-gesture recognition method, taking a data glove as input equipment, wherein the data glove can obtain rotation angle data of m joints. The micro-gesture recognition method comprises the following steps: a, firstly, carrying out data preprocessing, and carrying out curve fitting processing on the obtained data by using a least square method; b, carrying out gesture modeling, so that each gesture is represented by a discrete sub-sequence SLZ containing m elements; and c, carrying out gesture recognition, and carrying out gesture matching and recognition by using anediting distance based on template matching. According to the micro-gesture recognition method, the identification direction of the fine actions of the steering fingers is identified from traditionallarge-amplitude actions such as palms and arms, and a more natural and harmonious solution is provided for human-computer interaction in a universal environment.

The invention discloses a parameter-free hand back vein maximum circular region extraction method, and belongs to the technical field of biological feature recognition. The invention aims to provide aparameter-free hand back vein maximum circular region extraction method for performing direction correction on a hand back maximum circular region so as to extract and correct the maximum circular region of the hand back region. The method comprises the following steps: (1) carrying out binarization processing on a dorsal vein image by using a conditional random field image segmentation algorithm; (2) calculating Delaunay triangulation of the coordinate sequence by using an MATLAB function delaunay and triangulation; (3) finding out a maximum radius and a corresponding circle center coordinate to obtain a maximum circular area image Cmax of the hand back; (4) selecting a contour point near the little finger, and calculating the distance between the contour point and the circle center, namely the vertex of the little finger; and (5) calculating an included angle between the OA direction and the horizontal direction. The method has the advantages that the maximum circle extraction method does not need to carry out parameters and is low in time complexity; the invention provides a method for correcting the direction of a circular area.

The invention discloses an image encoding and decoding technology in the field of computers, and particularly relates to an image encoding and decoding method based on wavelet transform, and the method comprises the following steps: during encoding, performing wavelet transform on an image to be encoded for N times according to a wavelet transform model to obtain a wavelet coefficient, wherein thewavelet transform model is realized based on a CNN; quantizing the wavelet coefficients, so as to obtain a quantized wavelet coefficient; carrying out the entropy coding on the quantized wavelet coefficient to obtain a compressed code stream; during decoding, carrying out the entropy decoding on the compressed code stream to obtain a reconstructed wavelet coefficient; carrying out the inverse quantization on the reconstructed wavelet coefficient to obtain an inverse quantized wavelet coefficient; according to a wavelet transformation model, carrying out the wavelet inverse transformation on the wavelet coefficient after inverse quantization for N times to obtain a reconstructed image, wherein the wavelet transformation model is realized based on the CNN. By adopting the embodiment of theinvention, the coding and decoding performance of the image can be improved.

The invention provides an intelligent generation method for a three-dimensional model of a truss structure. By adopting staged intelligent aided design of a lapped frame structure, three-dimensional assembly modeling of the lapped frame structure is divided into two processes of qualitative lapping relationship judgment and quantitative geometric calculation, and by using a deep learning and Zhu-Liu algorithm combined method, the key first stage, namely, the qualitative lapping relationship judgment process is solved; then by using a geometric calculation method, the second stage is finished;and finally intelligent three-dimensional modeling of the truss structure is realized. According to the method, the three-dimensional assembly model of the truss structure can be effectively and intelligently built. The used method is relatively good in performance; the time complexity of an algorithm is relatively low; a modeling result is accurate and reliable; the built three-dimensional assembly model is better kept consistent with an existing design principle; and the generated frame structure model better conforms to current reality.

The invention provides a remote sensing satellite data transmission resource scheduling method and device based on bidirectional degree of freedom. The method comprises the following steps: determining an observation time interval of a remote sensing satellite, and calculating a receiving time period of each data transmission resource to the remote sensing satellite; calculating a receiving degreeof freedom corresponding to each observation task based on satellite observation station forecasting; selecting the observation task with the minimum receiving degree of freedom for analysis, and calculating the service degree of freedom of each data transmission resource for receiving the observation task; allocating the data transmission resource with the minimum service freedom degree to the selected observation task; deleting the observation tasks to which the data transmission resources have been allocated, and deleting the allocated data transmission resources; and repeating the processuntil all observation tasks are allocated with data transmission resources. The satellite data transmission resource scheduling method and device based on the two-way degree of freedom provided by the invention have the characteristics of simple judgment logic and low time complexity in allusion to conflict resolution of data transmission resource use by different remote sensing satellites.

The invention provides an image out-of-focus deblurring method based on a deep perception network. The method comprises the following steps: S1, preprocessing an image to be deblurred; S2, inputting a to-be-deblurred image into the trained depth perception network model to obtain a recovered image; S3, comparing the obtained restored image with a real clear image, and calculating a measurement index PSNR. According to the method, the auxiliary defocus blurring recovery of the depth image is effectively utilized, and the network is trained through enough data to learn degradation mapping, so an effective and rapid defocus deblurring image recovery network is obtained, and the method can be applied to image processing of a camera.

The invention discloses a method, a device and a system for selecting a central website, and relates to the technical field of computers. A specific embodiment of the method comprises the steps of constructing a multi-level portrait of a client based on historical data of the client of a website; obtaining a website customer group portrait according to the multi-level portraits of all the clientsof the website; obtaining a website attribute portrait according to the own attribute of the website; obtaining a website service portrait according to the service data of the website; and based on the website customer group portrait, the website portrait and the website service portrait, extracting features from the portraits, and selecting a central website through an FM model. According to theembodiment of the invention, a multi-level dot portrait is constructed, and an FM model with good performance on sparse data is adopted.

The invention discloses an infrared image sea-sky-line detection method based on a linear lookup matrix. The method comprises the steps: firstly carrying out column gradient calculation of an image in a large scale through customized template operation, and increasing the gray value of an image of a sea-sky-line region; secondly carrying out the processing of the image through employing a nonlinear segmentation method, and reducing the interference from water surface ripples and noise; finally carrying out the scanning of the image, generating the linear lookup matrix, and finding the maximum value of the matrix so as to locate the sea-sky-line position. The method is simple in implementation, is small in calculation amount, is clear and definite in physical significance, can detect horizontal and inclined sea-sky lines, and is easy to use in engineering. The test indicates that the method can effectively extract the sea-sky line in an infrared image, and has the certain anti-interference capability.

The invention discloses a method and device for scheduling delivery time, and relates to the technical field of computers. One specific embodiment of the method comprises the following steps: storing resource configuration data and reservation data into a range mapping storage structure according to a configuration time period; matching configuration time periods in the range mapping storage structure for the performance time periods in the performance time period set so as to determine the highest performance quantity and the task list quantity of the performance time periods; based on the highest fulfillment amount of the fulfillment time period and the number of the task orders, scheduling delivery time for the user. According to the embodiment, the allocation and reservation conditions of the reservation resources can be analyzed, the reservation resources are finely managed, the time complexity of the calculation process is reduced, and the calculation performance and maintainability are improved.

The invention discloses a heterogeneous graph data-oriented online graph division method. The method comprises the following steps of: 1) evaluating the calculation speed imbalance and the storage space imbalance of a graph calculation system; determining the calculation speed imbalance of the graph calculation system according to the node function time complexity T of different types of nodes in the heterogeneous graph calculation of the graph calculation system; according to the storage space Sv occupied by the data carried by the different types of nodes and the storage space Se occupied by the data carried by the different types of edges in the heterogeneous graph calculation performed by the graph calculation system, determining the storage space imbalance of the graph calculation system; and 2) according to the node function time complexity T corresponding to different types of nodes, the storage space Sv and the storage space Se corresponding to different types of edges, distributing the current to-be-processed heterogeneous graph data to different partitions. According to the method, the task allocation in the graph calculation is optimized, so that the load and memory use in the graph calculation process are more balanced, and the result of improving the running efficiency of the graph calculation is achieved.

The invention discloses a method and system for optimizing starting and stopping of a thermal power generating unit in a wind-solar-thermal storage system, and relates to the technical field of wind-solar-thermal storage resource optimization. The method comprises the following steps: determining the power generation amount of a thermal power generating unit in each preset time period according to the obtained wind power historical power generation amount, photovoltaic historical power generation amount, power grid historical demand, a preset energy use principle and a preset constraint condition in each preset time period all the year round; calculating the minimum annual investment and operation cost of the thermal power generating unit under a group of preset constraint conditions and a corresponding starting state sequence of the thermal power generating unit according to the generating capacity of the thermal power generating unit; the method comprises the following steps: acquiring a plurality of minimum annual investment and operation costs and a starting state sequence by adjusting a preset constraint condition, and selecting the annual minimum investment and operation cost with the minimum cost and the corresponding starting state sequence of the thermal power generating unit from the plurality of minimum annual investment and operation costs; according to the scheme, accurate shutdown or startup of the thermal power generating unit is realized, the operation cost of the thermal power generating unit is reduced, and the utilization rate of wind and light resources is improved.

The invention provides a business product information pushing method and device and a storage medium, and the method comprises the steps: obtaining business product information, carrying out the grouping of the business product information based on a preset grouping rule, and generating a plurality of business product groups; obtaining historical business volume information of the user who changes the business product, and generating a training set corresponding to the business product group based on the historical business volume information; performing clustering calculation on the training set, and obtaining a target business product group corresponding to the user according to a clustering calculation result; and pushing the business product information in the target business product group to the user based on a preset recommendation rule. According to the method, the device and the storage medium disclosed by the invention, the determination of the target business product group does not depend on any user, the business volume of the user does not need to be estimated, the pushing of the business product information is more accurate, and the time complexity of calculation is low.

The invention provides a graph matching association method and system for multi-radar dense targets. The method comprises the following steps: constructing a vector set formed by radar tracking targets; analyzing the influence of errors on approximate congruency, and forming a vector similarity matrix; giving a single-moment graph matching algorithm calculation process, and determining a triangle with the highest approximate congruency as an alternative triangle in a vector similarity matrix by using a greedy algorithm; the vertexes and the basic edges are screened from other targets to form alternative associated triangles; and designing an association relationship maintaining method under continuous moments, judging successfully associated triangles at historical moments and current moments by using a DS evidence theory, and determining a target corresponding to a final successfully associated triangle at the current moment, so that the association relationship maintaining stability and the association accuracy are improved. The method has the advantages of being low in time complexity, high in convergence speed and high in real-time performance, and applications such as multi-radar target fusion track generation can be effectively supported by establishing the stable and correct target incidence relation.

The invention provides an information interaction topology optimization method and a device for an unmanned aerial vehicle cooperative situation awareness network, and relates to the field of unmannedaerial vehicle communication. The method comprises the following steps: acquiring a collaborative situation awareness network of an unmanned aerial vehicle; obtaining a minimum spanning tree based onthe collaborative situation awareness network, and obtaining an undirected graph based on the minimum spanning tree; deleting the minimum spanning tree from the collaborative situation awareness network to obtain a communication network; acquiring a three-dimensional linear independent graph based on the undirected graph; and obtaining a three-dimensional rigid graph based on the three-dimensional linear irrelevant graph and the communication network, wherein the three-dimensional rigid graph is the information interaction topology of the unmanned aerial vehicle cooperative situation awareness network. According to the optimization method provided by the invention, the efficiency of cooperatively executing the situation awareness task by the unmanned aerial vehicle is improved.

The invention discloses a fast classification and recognition method based on small moving objects, According to the characteristics of small target, the contour of moving target is extracted first, and then the contour is fitted with rectangle and ellipse. The shape describing parameters of contour, rectangle and ellipse are combined to form a new robust shape describing operator. Finally, a treeclassifier is designed to classify the moving objects. The moving objects are divided into three categories: pedestrians, cyclists and automobiles. The tree classifier not only has low time complexity, but also can improve the robustness in different situations on the premise of guaranteeing the accuracy of classification.

The invention discloses a DDoS attack detection method for a high-speed network packet sampling data acquisition scene, and the method comprises the following steps: sampling network packets by using an equidistant sampling technology, and then quickly extracting effective characteristics of UDP traffic and TCP traffic by using two improved Call-Min Sketch structures comprising a plurality of counters and hash tables respectively; constructing UDP Flood and SYN Flood traffic classifiers for the UDP traffic and the TCP traffic in an off-line mode through a decision tree method of machine learning; utilizing the constructed classifiers, employing a Sketch structure for recording feature vectors of sampling packet flows online, and achieving the online detection of UDP Flood and SYN Flood of common DDoS attacks in a high-speed network packet sampling data collection scene. According to the method, DDoS attack can be accurately recognized in a high-speed network environment with low time complexity and space complexity, the UDP Flood detection method can well distinguish UDP Flood attack flow and DNS one-way request flow, the misjudgment rate is reduced, and the method can be used for safety monitoring of a high-speed network.

The invention relates to a fast symbol community discovery system for automatically determining the number of communities. The system comprises an adjacency matrix A and a network model(NModel) of a network N; the network model (NModel)=(n, K, Z, [pi], [theta], [omega]), wherein n is corresponding to the node number of the network; K is the number of models and corresponding to the number of communities in the network; Z is a n*K dimension matrix, wherein Zi is the indication vector of K dimension; [pi]={ [pi]1, [pi]-1, [pi]0} is a three dimension vector, wherein the [pi] 1 represents the connecting efficiency of positive linkage within the block, [pi]2 represents the connecting efficiency of negative linkage within the block and [pi] 0 represents the efficiency of zero linkage within theblock; [theta]={ [theta]1, [theta]-1, [theta]0} is a three dimension vector, wherein the [theta] 1 represents the connecting efficiency of positive linkage within the block, [theta]2 represents the connecting efficiency of negative linkage within the block and [theta] 0 represents the efficiency of zero linkage within the block; [omega] is a K-dimension vector which indicates the ratio relation ofnodes distributing in different blocks and the formula is satisfied: the summation of [omega]k equals to 1, wherein k is from 1 to K. Parallel computing of parameter estimation and model selection isrealized, which effectively reduces the time complexity of symbolic community mining.