Mutual constraint based fuzzy data classification method

Abstract

The invention discloses a mutual constraint based fuzzy data classification method which is used for information category mode setup and data category analysis. The mutual constraint based fuzzy data classification method is characterized by including steps: using an elasticity based four-point center and border line algorithm to construct a category rule (quintuple mode); using a constraint-based inching classification algorithm and an optimized classification rule algorithm based on self-training to optimize and adjust the category rule (quintuple mode). The method has special sample (currently unknown category) detection capability, is suitable for classification analysis and excavation of pervasive data, and is well adaptable to outliers, category topological irregularity and 'acute border' problems. Further, the method is applicable to classification and analysis of data sets which are large in data volume and cannot be read in a memory by one time, and has functions of autonomous category adjustment and identification. Compared with an existing method, the mutual constraint based fuzzy data classification method has the advantages that average recognition rate is up to 99.47%, average false alarm rate is only 5.2%, and operating speed is slightly lower than a traditional algorithm.

Description

technical field [0001] The invention relates to the field of computer information technology, in particular to a fuzzy data classification method based on mutual constraints. Background technique [0002] Fuzzy classification analysis is a very important research and application subject at present, and has a wide range of applications in engineering technology and economic fields. [0003] Fuzzy classification analysis is based on fuzzy theory to learn the data of known category information, obtain the pattern rules of the corresponding category, and then judge the category of the new data through the pattern rules. The key of fuzzy classification analysis is detection accuracy, ability to identify new data and certain generalization processing ability, the core of which is generalization processing ability. [0004] Many scholars have studied the generalization ability of fuzzy classifiers. From the topological point of view, there are currently three common fuzzy classifi...

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Problems solved by technology

[0008] In order to overcome the deficiencies in the background technology, a fuzzy data classification method based on mutual constraints is proposed, which is used to improve the generalization classification ability of the fuzzy classification algorithm, and solve the overlapping and large inflection points of the sets formed by heterogeneous samples in the case of complex data distribution. And the problem of data merging, improve the accuracy, adaptability and self-learning ability of fuzzy classification algorithm

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