448 results about "Nearest neighbor search" patented technology

The invention provides an enhanced generation method based on question matching retrieval, and belongs to the field of matching generation, and the method comprises the following steps: S1, a semantic feature coding stage: carrying out real-time feature extraction and vector space mapping on a natural language query input by a user by adopting a deep neural network model, generating high-dimensional distributed representation with semantic representation capability; s2, a knowledge base intelligent retrieval stage: executing multi-dimensional semantic matching in the vectorized knowledge base based on an approximate nearest neighbor search algorithm, and screening out a candidate knowledge set highly related to query semantics through a similarity measurement function; s3, retrieval matching results are automatically associated to the structured knowledge base through the established semantic-knowledge mapping relation, the preprocessed standardized response content is directly obtained, and the response content adopts a multi-modal data organization form and comprises a structured data entity and retains a rich text expression form.

The invention discloses an unmanned aerial vehicle cargo distribution management method and system based on strategy optimization, and particularly relates to the technical field of cargo distribution management. Through an improved incremental K-means clustering algorithm, a KD-Tree index is combined to optimize nearest neighbor search, order area grouping efficiency is improved, based on an order cluster set O, mixed integer programming and a Hungary algorithm are adopted to carry out unmanned aerial vehicle task matching, a task distribution moment M is optimized, a distribution path is dynamically adjusted to deal with real-time weather and airspace limitations, and in the distribution process, the distribution efficiency is improved. Data such as order liquidity index, battery consumption rate and task balance degree are acquired through an unmanned aerial vehicle sensor, and the scheduling delay condition is judged by using fuzzy logic reasoning, so that the system can still realize efficient, balanced and intelligent unmanned aerial vehicle scheduling in a high-load scene, the distribution delay is greatly reduced, the no-load rate is reduced, and the overall logistics efficiency is improved.

The invention provides a fabricated building quality monitoring system based on BIM and point cloud integration, and relates to the technical field of data processing, and the system comprises a BIM model feature point obtaining module which is used for obtaining BIM model feature points; the point cloud feature point obtaining module is used for obtaining point cloud feature points; the preliminary matching result obtaining module is used for carrying out descriptor matching and matching pair screening; the optimization matching result obtaining module is used for performing evaluation transformation and error calculation; the fine alignment result obtaining module is used for carrying out preliminary transformation calculation, nearest neighbor searching and error minimization; and the quality improvement suggestion generation module is used for carrying out error analysis and manual correction and carrying out monitoring and quality tracing. According to the method and the device, the technical problem of insufficient feature point extraction and matching precision in the prior art can be solved, and the technical effect of improving the alignment precision of the point cloud data and the BIM model by optimizing the feature point extraction and matching precision is achieved.

The invention relates to a public data automatic classification and grading method and system, and belongs to the field of data automatic classification and grading. Comprising the following steps: constructing a classification and grading vector knowledge base; for input data to be classified and graded, similarity matching is carried out on the basis of a nearest neighbor search algorithm and the content of the classification and grading vector knowledge base, and matching information of the field to which the data belongs and the grading rule is obtained; carrying out pre-training on the large model, and then carrying out adaptive fine tuning on the pre-trained large model based on the public data classification and grading vector knowledge base; and inputting data to be classified and graded and matching information of the classification and grading vector knowledge base into the large model for automatic classification and grading, and outputting a classification and grading result. According to the method, the problem that the existing classification and classification lack an automatic method and need to depend on a large amount of manual customized services is solved, automatic public data classification and classification are realized, and the generalization and efficiency of classification and classification are improved.

The invention discloses a literature semantic search method and system based on elastic search, and relates to data retrieval. The literature semantic search method comprises the steps that vectorization processing is conducted on a noun phrase list by means of a text2vec-based-multilingual model trained based on a CoSENT method; according to the semantic vector, performing approximate nearest neighbor search in a second retrieval module to obtain first candidate data; inputting the query text data into a first retrieval module, and performing keyword matching through a BM25 algorithm to obtain second candidate data; fusing the first candidate data and the second candidate data to obtain third candidate data; a Sequence Matcher algorithm is adopted to calculate character string similarity between expansion words in the third candidate data, a similarity threshold value is set based on the length of the longest common subsequence, duplicate removal is carried out, and fourth candidate data is obtained; and performing weight distribution based on positions and similarity scores on the fourth candidate data, and enhancing the distinction degree of the extension words by expanding a score interval to obtain extension word recommendation list data. According to the method, the accuracy of document retrieval is remarkably improved.

The invention discloses a document analysis method based on an RAG model and a dynamic knowledge graph, and relates to the technical field of artificial intelligence. The method is combined with an RAG model and a dynamic knowledge graph technology, and is realized by the following steps of: performing entity relationship joint extraction on an input document, generating a structural triple, and constructing a dynamically updatable knowledge graph; based on the knowledge graph, mapping entities and relationships into low-dimensional vectors by adopting a graph embedding model, and constructing a local vector knowledge base with a topological structure; receiving user questions in real time, encoding the user questions into query vectors, executing approximate nearest neighbor search based on the vector knowledge base, and matching related map fragments; and combining the retrieved graph fragments with the large language model, and generating a structured answer through path constraint of the injection knowledge graph. The method is used for solving the problem that in the prior art, a model cannot capture document deep semantics and dynamic relations insufficiently.

The invention discloses an irregular roadway colored point cloud hole filling method, and belongs to the field of underground mine three-dimensional modeling and point cloud data processing. According to the method, firstly, dense slicing is performed on roadway point clouds, slices are projected to a two-dimensional plane for polar coordinate sorting, disordered point clouds are ordered, then the slices are used as data points, a control point solution matrix is solved by adopting a chasing method, initial control points are rapidly acquired, the control points are optimized by utilizing a progressive iterative approximation algorithm (PIA), and the point clouds are optimized by utilizing the PIA. According to the method, a fitting curve is obtained, the error between the fitting curve and an original point cloud is effectively reduced, non-uniform cubic B-spline adaptive fitting interpolation is carried out according to the point spacing to realize uniform filling of holes, finally, K-nearest neighbor search is carried out on interpolation points, the RGB mean value of neighborhood points is calculated and assigned to the interpolation points, and color repair of hole areas is realized. The method has a good repairing effect on various types of holes of the irregular roadway of the mine, uniform color assignment can be carried out on point clouds at the same time, and the modeling quality and efficiency are effectively improved.

The invention discloses an automatic planning method and system for a curved surface path of a grinding robot, and the method comprises the following steps: S1, constructing a to-be-machined three-dimensional curved surface model and a triangular mesh model, and relates to the technical field of automatic control of robots for surface machining in the manufacturing industry. According to the method, a complex three-dimensional surface coverage path planning problem of a workpiece is converted into two low-complexity sub-problems of key point sampling of a two-dimensional path planning domain and three-dimensional shortest path searching on a triangular mesh model, a small number of sampling points are sampled only on the two-dimensional path planning domain, and the sampling process depends on a spatial index framework; according to the method, global traversal of disordered point clouds is avoided, three-dimensional coordinates are positioned through nearest neighbor search of a KD tree algorithm, adjacent three-dimensional ordered key points are sequentially connected through a graph search algorithm, and a continuous machining path is formed, so that the operand is greatly reduced, real-time response of path planning is facilitated, and the problems that traditional online planning is long in time consumption and difficult in real-time response are solved.

The invention discloses a sparse view angle-oriented three-dimensional Gaussian visual positioning method, which belongs to the technical field of positioning, is used for visual positioning, and comprises the following steps of: extracting global image features and local image features of a sparse image, and constructing a feature database; performing nearest neighbor search on the feature database and the RGB image to obtain a sparseness index, calculating a camera pose of a pseudo view angle, rendering a color image and a depth image under the pseudo view angle by using a training main model, extracting global features and local features and storing the global features and the local features into the feature database, constructing a 2D-3D corresponding relation in combination with a target color image, and performing dynamic inner point screening and pose solving. And obtaining a positioning result. According to the method, visual angle deficiency of a key area is intelligently completed, the balance and coverage of feature expression in a three-dimensional scene are effectively improved, and dynamic updating and mismatching elimination of a 2D-3D matching relation are realized, so that the positioning precision and the system stability are remarkably improved, and the method has higher robustness and actual availability under a sparse training condition.

The invention provides a question and answer pair generation method, electronic equipment, a storage medium and a computer program product, and the method comprises the steps: dividing a to-be-processed text in a target knowledge base to obtain a plurality of paragraphs; for each paragraph, extracting a plurality of keywords for the paragraph; constructing a first input parameter for each paragraph and the plurality of keywords corresponding to the paragraph, and processing the first input parameter through a specified large model to obtain a question corresponding to the paragraph; according to the plurality of keywords corresponding to each paragraph, recalling a plurality of related paragraphs corresponding to the plurality of keywords from the plurality of paragraphs through approximate nearest neighbor search; based on a plurality of related paragraphs corresponding to each paragraph, respectively constructing a second input parameter for each question of the paragraph, and processing the second input parameter through a specified large model to obtain an answer corresponding to the question; and constructing each question corresponding to the to-be-processed text and the answer corresponding to the question into a question-answer pair. According to the scheme, high-quality question and answer pairs can be generated.

The invention discloses a few-sample visual defect detection method based on approximate proximity search, and belongs to the technical field of industrial intellectualization. The system mainly comprises an image acquisition module, a feature extraction module, a memory bank construction module, a feature compression module, an approximate nearest neighbor search module and an anomaly detection and result output module. The feature extraction module is used for performing feature extraction on the collected image; the feature compression module is used for reducing the feature data volume and reducing the internal memory and calculation burden; the memory bank construction module is mainly operated during system initialization or model training and is used for constructing a feature memory bank of normal samples; the approximate nearest neighbor search module is responsible for quickly finding normal sample features closest to the input features in the memory bank; and the anomaly detection and result output module is used for converting distance information obtained by neighbor search into anomaly. According to the few-sample visual defect detection method based on the approximate proximity search, the effect of few-sample anomaly detection is achieved.

Provided are systems, methods, and apparatuses for systems and methods of memory efficient multi-vector information retrieval based on embeddings from a storage pipelined network. In one or more examples, the systems, devices, and methods include performing a first portion of a nearest neighbor search on a first subset of a set of media sources; performing a fetch process on the first subset that include identifying a first set of highest matching media sources from the first subset, transferring multi-vector representations of the first set of highest matching media sources from a storage drive to a memory, and performing a second ranking of the first set highest matching media sources. The systems, devices, and methods include performing, in parallel with the fetch process, a second portion of the nearest neighbor search on a second subset of the set of media sources.

The invention provides a retrieval enhancement generation method and system for multi-modal semantic alignment in the technical field of computer vision and natural language processing. The method comprises the steps that S1, a large amount of text data, image data and 3D model data are acquired to construct a multi-modal vector knowledge base; s2, acquiring an input query image and a natural language question, and performing joint semantic coding on the query image and the natural language question to obtain a multi-modal query feature vector; s3, evaluating complexity, dynamically adjusting retrieval parameters based on the complexity, and retrieving Top-K candidate knowledge from the multi-modal vector knowledge base through an approximate nearest neighbor search method based on the retrieval parameters and the multi-modal query feature vectors; and S4, in combination with multi-dimensional similarity and token-level interaction alignment, screening each candidate knowledge, and outputting a retrieval result. The method has the advantages that the reasoning precision, the answer credibility and the response efficiency in a complex question and answer scene are greatly improved.

The invention discloses a tunnel surrounding rock multi-source data dynamic fine rapid sensing method, which comprises the following steps: based on a mobile terminal multi-view image sequence, arranging shooting positions according to a preset space interval and an orthogonal angle, generating a sparse point cloud through a motion recovery structure algorithm, and generating a dense point cloud model in combination with multi-view stereo matching optimization; and for the dense point cloud model, delimiting a local neighborhood based on k-nearest neighbor search, resolving a neighborhood point covariance matrix through principal component analysis, extracting a feature vector corresponding to a minimum feature value as a normal vector, and constructing a rock mass surface microscopic geometric feature field. The invention provides a dynamic fine rapid sensing method based on multi-source data, and aims to improve the efficiency, precision and timeliness of tunnel surrounding rock information acquisition and provide accurate surrounding rock information support for tunnel construction and support design.

To accelerate search speeds for approximate nearest neighbor searches of vector databases, compute-in-memory techniques using NAND memory structures are introduced. For each element of the database, a kernel of its M nearest neighbors is determined. For each vector of the database, both the vector and its kernel are programmed in the arrays of a NAND memory based accelerator card, so that the vectors will be written into the memory arrays both as themselves and also in kernels of vectors for which they are a nearest neighbor. Metadata, associating the locations of the kernel members with the correspond vector is also stored in the memory system. After determining the input's nearest neighbor at one level of search, the metadata is then used to locate that nearest neighbor's nearest neighbors and their distances to the input vector are then computed in parallel in a compute-in-memory vector-vector dot product multiplication.

The invention discloses an obstacle avoidance motion planning method and system for redundant drive of an intelligent process of a welding robot and computer equipment. According to the method, firstly, a motion path of a robot is planned based on welding process redundancy, then a sampling strategy that the position and posture of a welding gun are separated is adopted, then the sampled posture is mapped to a planar two-dimensional space, and the relevance between an obstacle collision boundary and the obstacle avoidance posture of the welding gun is constructed; an incremental extreme gradient lifting model is adopted to carry out probability modeling on the collision boundary and obstacle avoidance attitude relevance, nearest neighbor search and new planning node expansion are carried out based on target cost to obtain new planning nodes, collision detection is carried out on the new planning nodes, and the new planning nodes passing the collision detection are used for incremental learning of an obstacle avoidance model; and finally, an optimization strategy of spherical linear interpolation and cosine slow motion time mapping is adopted to ensure continuity and smoothness of a motion planning path trace. The problem that in the prior art, it is difficult to quickly calculate and generate a collision-free welding track is solved.

The described technology is generally directed towards homoglyph attack detection. A homoglyph attack detection service can create images of customer's protected domain names. A convolutional neural network can generate feature vectors based on the images. The feature vectors can be stored in a similarity search data store. Newly observed domain names can be compared to the customer's protected domain names, by also generating feature vectors for the newly observed domain names and conducting approximate nearest neighbor searches. Search results can be further evaluated by comparing protected domain names to newly observed domain names using a siamese neural network which applies a similarity threshold. Newly observed domain names that meet or exceed the similarity threshold can be flagged for further action.

The invention relates to the technical field of robot path planning, and discloses an improved robot path planning method based on an RRT algorithm, and the method comprises the steps: carrying out the global path searching through an improved fast exploration random tree (RRT) algorithm, and employing a special random sampling strategy and a KD-Tree to accelerate the nearest neighbor searching, and improving the searching efficiency; a greedy strategy is utilized to optimize a path, and redundant nodes are reduced; a smooth trajectory is generated by means of cubic spline interpolation, and control points are dynamically adjusted through collision detection. The method effectively solves the problems of path redundancy and poor smoothness of a traditional RRT algorithm, is suitable for the fields of robot navigation, logistics transportation and the like, and can remarkably improve the path planning efficiency and quality.

The invention discloses an electric power multi-modal corpus construction query method and system based on a sliding window, which is applied to the field of electric power data query, and comprises the following steps: obtaining a structured document according to electric power multi-modal data, segmenting the structured document to obtain a plurality of segmented text blocks, and storing the segmented text blocks into a database; inputting each segmented text block into a large language model to generate a to-be-stored text vector and construct an electric power multi-mode corpus, when a user query request is received, generating a plurality of query variants according to query data, performing nearest neighbor search on each query variant in the electric power multi-mode corpus to obtain a corresponding nearest neighbor search result, and storing the nearest neighbor search result in the electric power multi-mode corpus. And fusing each nearest neighbor search result to generate an electric power related document set comprising a multi-modal association mark. According to the method, the semantic units can be accurately captured, semantic breakage is avoided, the retrieval continuity and coverage rate are improved, the comprehensiveness and context adaptability of retrieval results are improved, and the data retrieval requirement under the complex scene of the power industry is met.

The invention discloses a roller coaster track gauge measurement method and system based on three-dimensional point cloud data, and relates to the technical field of roller coaster track gauge measurement, and the method comprises the steps: firstly obtaining the three-dimensional point cloud data of a space where a target roller coaster track is located, and extracting the three-dimensional point cloud data of the target roller coaster track from the three-dimensional point cloud data; based on the cross section features of the orbits, center track point cloud data of the two single-side orbits are extracted from the cross section features, the center track point cloud data of the two single-side orbits are matched with each other through a nearest neighbor search algorithm, a plurality of point cloud pairs are obtained, the distance between the two point clouds in each point cloud pair is calculated, and the distance between the two point clouds in each point cloud pair is calculated; therefore, the track gauge of any position of the target roller coaster track can be determined. According to the scheme, the three-dimensional point cloud data of the roller coaster track is obtained, the track gauge can be automatically measured and calculated through a designed algorithm, manual measurement is not needed, and calculation is accurate; moreover, the distance between the central points of the two rails can be measured, and the value of the rail gauge data is greatly improved.

The invention discloses an AIGC-based digital media content recommendation system, which comprises the following steps of: firstly, interpreting a series of behavior events displayed by a user in a current session in real time by using a large language model (LLM) subjected to instruction fine tuning so as to intelligently generate a text capable of accurately describing the current dynamic intention of the user; this enables the system to sensitively capture subtle and instant interest focuses of the user; then, converting the dynamic intention description into a vectorized representation by adopting a text embedding model to obtain an intention vector, further performing efficient approximate nearest neighbor search in a pre-constructed content library vector index by utilizing the intention vector, so as to quickly position and screen out candidate content highly matched with the current intention of the user, and finally, extracting the current intention of the user from the candidate content. And selecting a plurality of most relevant items from the candidate list to obtain a final recommendation list. Therefore, the system can significantly optimize the content recommendation effect in the session scene by deeply utilizing the understanding of the LLM on the dynamic intention of the user and the generation capability of the AIGC, and improves the user experience.

The invention relates to the technical field of wading slope deformation monitoring, in particular to a deformation monitoring method and system applied to a rock wading slope and a medium, and the method comprises the steps: obtaining point cloud data of each region in the wading slope in each sampling period; determining a rock mass deformation index of each cluster corresponding to each area in the wading slope in each sampling period; determining the rock mass complexity, the slope instability value and the minimum segmentation threshold value of each area in the wading slope in each sampling period, and performing nearest neighbor search on the point cloud data of two adjacent sampling periods by combining a space division algorithm to obtain nearest neighbor point cloud data corresponding to each point cloud data; determining the slope deformation degree of each area in the wading slope in each sampling period; and the deformation of the rock wading slope is monitored and early warned. The method is beneficial for improving the searching precision of the nearest neighbor point cloud data, reducing the subsequent calculation error, and improving the monitoring precision of the region with the high deformation risk in the rock wading slope.

Systems and methods are directed to incorporating approximate nearest neighbor search as implicit edges in a knowledge graph. The system generates an approximate nearest neighbor (ANN) index that indexes entities by their embeddings. The system models a knowledge graph by including the embeddings as nodes in the knowledge graph. Based on a search query, the system performs a search of the knowledge graph to obtain results, whereby performing the search includes traversing one or more implicit edges from a node of an embedding in the knowledge graph to one or more related nodes in semantic vector space based on the ANN index. The results are then presented on the device of the user.

The invention relates to the cross technical field of artificial intelligence and data retrieval, and particularly provides an RAG question and answer optimization method and device.Firstly, original knowledge documents of various formats and natural language query of a user are received on an input layer, then a knowledge base construction module and a knowledge retrieval module are arranged on a processing layer, and a knowledge retrieval module is arranged on the processing layer; the middleware layer is provided with an MCP Server module and a vector database, the MCP Server module is a standard interface agent, and the vector database is a distributed storage engine for efficient approximate nearest neighbor search and mixed retrieval; and finally, deploying an LLM generation module at an output layer for generating a final answer based on the retrieved enhanced context. Compared with the prior art, the method has the advantages that irrelevant or low-signal-to-noise-ratio knowledge contacted by a large language model (LLM) can be effectively reduced, and the accuracy, the reliability and the practicability of knowledge base questions and answers are comprehensively improved.

An online system applies a visual language model and an optical character recognition model to a received image to generate descriptive information about unknown items in the image. The online system prompts a generative model with the descriptive information about unknown items in the image to separate the descriptive information into different bins each corresponding to a different unknown item in the image. For each unknown item detected in the image, the online system generates a target embedding from its descriptive information and performs a nearest neighbor search on an item catalog including embeddings for various items to find a set of candidate embeddings matching the target embedding. The online system retrieves item attributes of candidate items each corresponding to a candidate embedding of the set and prompts the generative model with this information to rank candidate items for the unknown item in the image.

The invention discloses an automatic SCADE test case generation method based on a large language model, which comprises the following steps of: analyzing various SCADE related documents to obtain an analysis result; the analysis result is converted into JSON data; performing key information extraction on the JSON data, and performing block processing on the extracted key information to obtain text blocks; vectorizing the text blocks, storing the vectorized text blocks into a vector database, converting a natural language query requirement of a user into a query vector, and performing approximate nearest neighbor search with the query vector in the vector database by utilizing RAG retrieval to obtain a preliminary retrieval result; based on metadata in the preliminary retrieval result, filtering, timeliness sorting and deduplication fusion processing are carried out, and a retrieved context information set is obtained; adopting a dynamic cue word template to construct an enhanced cue word based on the retrieved context information set and the natural language query of the user; and calling a large language model API, inputting an enhanced cue word, and generating a structured test case.

The invention discloses an intelligent medical big data information management system, and the system comprises a data collection layer which serves as a sensing neural network, employs an Internet of Things gateway to be compatible with a medical equipment protocol, and deploys an edge calculation node for physiological signal preprocessing; the intelligent preprocessing layer is used for cleaning and standardizing the original data transmitted by the data acquisition layer; the informatization service layer is used for realizing fine-grained authority control based on an RBAC (Role Based Access Control) model, realizing inspection process automation by integrating an LIS (Line Information System) interface through a Hyperdger Fabric block chain evidence storage key operation hash, and constructing a zero-footprint DICOM (Digital Imaging and Communications in Medicine) viewer; the intelligent analysis layer is used for carrying out data life cycle management by adopting a mixed storage architecture, carrying out feature engineering on data, carrying out label matching and classified storage by using efficient neighbor search and similarity / distance measurement, quantifying contribution of each feature or feature combination to a single prediction result through an SHAP value, generating an interpretation result, and outputting the interpretation result; and presenting an interpretation result to a clinician or related personnel through a query interface.

The invention discloses a vehicle contour detection method and system based on a laser radar, a high-frequency (60Hz) laser radar sensor is adopted to scan a vehicle, the system can generate high-resolution point cloud data, fine structural features of the vehicle can be captured through high-frequency scanning, the contour measurement precision is ensured, and the detection precision is improved. The point cloud data processing unit utilizes advanced algorithms (such as a kd-tree-based neighborhood averaging method and a least square method) to carry out denoising and boundary point identification, the accuracy of a measurement result is further improved, through the methods, the system can accurately identify and measure the length, the width and the height of the vehicle, errors are controlled within a minimum range, and the accuracy of the measurement result is improved. The system adopts an efficient kd-tree structure to manage and process point cloud data, the data processing speed is remarkably improved, and the kd-tree has excellent performance in the aspects of spatial query and nearest neighbor search, so that data processing is more efficient.

The invention discloses a data fusion method, system and device for power transmission and transformation equipment and a medium, and the method comprises the steps: obtaining the data of the power transmission and transformation equipment through a sensor, and extracting a multi-scale visual feature set based on a Gaussian filter; fitting the laser point cloud data through a principal component analysis method to generate a multi-scale point cloud feature set, and performing feature matching through a nearest neighbor search algorithm; and converting the visual image and the laser point cloud data into a common coordinate system, and fusing the laser points and the pixel points according to a preset weight to generate multi-source fusion data. According to the invention, by fusing the visual image and the laser point cloud data, the multi-dimensional information of the power transmission and transformation equipment can be obtained, and a more comprehensive and more accurate digital twinborn model is constructed; the subtle change of the power transmission and transformation equipment can be more accurately captured through multi-source fusion data, and the dynamic sensing of the operation state of the equipment is realized; through the multi-scale fusion method, complementary information in multi-source data can be effectively utilized, and the precision of the digital twin model is improved.

Low latency search for nearest neighbors in a dataset containing a large number of entries is improved using an error correction code (ECC) for partitioning data into clusters and retrieval. During initialization and preprocessing a d-dimensional space with clusters corresponding to ECC codewords is specified. Entries in the dataset are embedded into this space and associated with respective codewords, each codeword specifying a cluster. An index associates the codewords, clusters, and entries. During a query of the dataset, a query entry is processed to determine a query embedding in the d-dimensional space. The query embedding is used as input for a list decoder of the ECC. The list decoder provides a set of nearest codewords, with those codewords representing a set of candidate clusters that may contain nearest neighbors. The dataset entries associated with the candidate clusters may then be searched to determine query results comprising specific entries.