Abstract
In order to realize efficient data processing in wireless network, this paper designs an automatic classification algorithm of multisearch data association rules in a wireless network. According to the algorithm, starting from the mining of multisearch data association rules, from the discretization of continuous attributes of multisearch data, generation of fuzzy classification rules, and the design of association rule classifier and other aspects, automatic classification is completed by using the mining results. Experimental results show that this algorithm has the advantages of small classification error, good real‐time performance, high coverage rate, and high feasibility.
Highlights
Along with the computer science and technology, especially the rapid development of database technology, as well as the expansion of the scope of human activities and life rhythm speeding up, people can more quickly and more in a more cheap way, obtain and store data, making people increase the ability of generating and collecting data; this makes the data and information indices increase [1]
We propose an improved fuzzy associative classification algorithm, the fuzzy C-means clustering algorithm for continuous attribute fuzzy interval, obtaining high-quality fuzzy classification rules, and on this basis, to join a new pruning strategy to avoid generating useless rules, rules at the same time, using a new importance measure to fusion of multiple fuzzy classification rules, in order to improve classification accuracy
The algorithm starts from the mining of multisearch data association rules and uses parameters to measure the importance of fuzzy classification rules
Summary
Along with the computer science and technology, especially the rapid development of database technology, as well as the expansion of the scope of human activities and life rhythm speeding up, people can more quickly and more in a more cheap way, obtain and store data, making people increase the ability of generating and collecting data; this makes the data and information indices increase [1]. Based on the neural network optimization algorithm, Wang Yongcheng et al from Shanghai Jiao Tong University studied the Chinese text classification system. The innovation of this paper lies in the fact that association classification is based on association rules, which reflects the application characteristics of knowledge and reflects the intrinsic correlation characteristics of knowledge This process is mainly embodied in two aspects: the mining method of multisearch data association rules in a wireless network and how to analyze and classify the mining rules. Based on the above background, this paper designs an automatic classification algorithm for multisearch data association rules in wireless networks. The first section discusses the introduction part of the paper; the second section discusses the classification algorithm; the third section conducts an experimental analysis; and the fourth section summarizes the paper
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