Fuzzy clustering evaluation method based on dichotomy modularity

An evaluation method and fuzzy clustering technology, applied in character and pattern recognition, instrument, calculation, etc., can solve the problem that the accuracy rate needs to be improved, and achieve the effect of enhancing the robustness and improving the accuracy rate.

Active Publication Date: 2019-08-06
HENAN POLYTECHNIC UNIV
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Problems solved by technology

Some widely used effectiveness indicators such as PC, PE, and MPC are too dependent on the membership degree generated by the FCM algorithm, and because the FCM algorithm itself is

Method used

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Embodiment Construction

[0016] A fuzzy clustering evaluation method based on binary modularity, comprising the following steps:

[0017] (1) Run the FCM algorithm on a data set with N data points to obtain C clustering result clusters and the membership matrix u of the i-th data point to the c-th cluster ci (i=1,2...,N; c=1,2...C);

[0018] (2) Calculate the intra-class compactness, and for each data point, calculate the sum of the squares u of its membership to all clusters c 2 i , compare the results of all data points and get the maximum value u max . For all data points, calculate the ratio of the sum of the squares of its membership to all clusters to the maximum value;

[0019] (3) Calculate the separation between classes, use the membership degree of each data point to two different clusters, set the threshold T o Exclude noisy points and outliers on cluster boundaries. In the fuzzy membership degree matrix obtained by running the FCM algorithm, the sum of the separation degrees of all ...

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Abstract

The invention provides a fuzzy clustering evaluation method based on dichotomy modularity, which integrates intra-class compactness, inter-class separability and dichotomy modularity together and is used for determining an optimal classification result of a fuzzy C-means clustering algorithm. The index is combined with intra-class compactness and inter-class separability, the robustness of the index is enhanced, the optimal cluster number can be accurately detected, and the accuracy of evaluating the clustering result is improved.

Description

technical field [0001] The invention relates to a clustering evaluation method, in particular to a binary modularity-based fuzzy clustering evaluation method, which belongs to the field of data mining. Background technique [0002] As one of the key technologies of data mining, clustering can divide a set of samples into multiple clusters, so that the similarity between elements in the same cluster is as high as possible, while the similarity between elements in different clusters is as low as possible. [0003] Fuzzy clustering represented by the FCM (Fuzzy C Means) algorithm fuzzifies the value of the membership degree, allowing a sample to belong to multiple clusters with different probabilities, which is more in line with people's cognition of the distribution of samples, so fuzzy clustering Research is timeless. So far, a large number of fuzzy clustering algorithms have made continuous progress in accuracy, efficiency, robustness, etc., which have effectively promoted ...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/23213G06F18/24
Inventor 刘永利韩光伟郭倩倩陈敬丽杨合超
Owner HENAN POLYTECHNIC UNIV
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