Design method of multi-dimension attribute data oriented multi-layered clustering fusion mechanism
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A design method, multi-dimensional attribute technology, applied in computing, computer components, instruments, etc.
Active Publication Date: 2015-09-23
NANJING UNIV OF POSTS & TELECOMM
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[0005] The purpose of the present invention is to provide a design method of a multi-layer clu
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Embodiment 1
[0085] 1. Architecture
[0086] Such as figure 1 As shown, the present invention analyzes the characteristics of each stage of multi-sensor information clustering and fusion, and first sets a threshold to filter and delete abnormal points in the analysis domain data. The information clustering fusion architecture based on multi-dimensional mixed attributes mainly includes four parts: extraction of optimal reference standards based on index attributes, gray relational cluster analysis, application of rough set theory, and probability statistics data level.
[0087] 2. Method flow
[0088]In the wireless sensor network, the invention uses a group of sensor nodes to collect different types of information at the same time for a certain target, and processes the data information to extract valuable knowledge. Since the data sensed by sensor nodes may be missing or uncertain, the collected data is first preprocessed and converted into a matrix format, and then thresholds are set ...
Embodiment 2
[0157] 1. Analyze domain data for gray relational clustering
[0158] The analysis domain data clustering processing planning process is as follows:
[0159] 1) Collect a group of sensor system nodes in a monitoring area to monitor targets, obtain sensing data, and convert it into a matrix format through preprocessing. X={X i |X i =(X i1 ,...,X im ,class)}i∈N is the comparison object set of analysis domain data, Y={Y i |Y i =(Y i1 ,...,Y im )}(i=1,2,...,p) is a known reference sequence set. By setting the threshold, filter and delete the abnormal data points of the analysis domain data.
[0160] 2) According to the characteristics of data index attributes, extract the optimal reference standard X 0 .
[0161] 3) According to the attributes of each characteristic index, normalize the data in the analysis domain, eliminate the impact of dimension (unit), and compress each data object in the analysis domain to the [0, 1] interval.
[0162] 4) Take the resolution coeff...
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Abstract
The invention discloses a design method of a multi-dimension attribute data oriented multi-layered clustering fusion mechanism. The method comprises the following steps: 1) converting a data set into a matrix form, and preprocessing data; 2) according to data index attribute characteristics, extracting an optimal reference standard, and carrying out normalization processing on the data; 3) calculating a grey correlation degree, generating a similar matrix of the grey correlation degree, and then, carrying out grey correlation degree clustering to obtain a primary clustering result; 4) according to the primary clustering result in the step 3), adopting a rough set theory to establish a decision table system; 5) calculating an attribute significance information entropy of the decision system for each clustering member; 6) setting a weight for each clustering member; and 7) according to the calculated weight, adopting a probability method to calculate a probability of each data object in each class level to which the data object belongs, selecting the class level where the data object belongs to when the probability is highest to serve as the class level to which the data object belongs to, and obtaining a final clustering fusion result.
Description
technical field [0001] The invention relates to a design method of a multi-layer cluster fusion mechanism oriented to multi-dimensional attribute data, and belongs to the technical field of data mining. Background technique [0002] Clustering fusion technology is applied to analyze and process, mine data, and extract useful knowledge for the irregularity and dispersion of data. The clustering fusion algorithm is an unsupervised machine learning algorithm. Unlike the supervised learning algorithm, it does not require prior knowledge of the distribution of the data set. The purpose of the clustering algorithm is to divide the data into several categories to reveal the real situation of the data distribution. Usually, the data collected by a group of sensor nodes generally has multiple mixed attributes, and the data of multiple attributes are clustered and fused to avoid the blind area of single attribute data processing, improve the quality of multi-source information proc...
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