本文選題：學(xué)習(xí)分類系統(tǒng) + 進(jìn)化計(jì)算　； 參考：《計(jì)算機(jī)應(yīng)用》2017年11期

【摘要】：提出一種基于復(fù)雜學(xué)習(xí)分類系統(tǒng)(XCS)的密度聚類方法,可以用于對任意形狀且?guī)в性肼暤亩S數(shù)據(jù)進(jìn)行聚類分析。此方法稱為DXCSc,主要包括以下三個(gè)過程:1)基于學(xué)習(xí)分類系統(tǒng),對輸入數(shù)據(jù)生成規(guī)則種群,并對規(guī)則進(jìn)行適當(dāng)壓縮;2)將已經(jīng)生成的規(guī)則視為二維數(shù)據(jù)點(diǎn),進(jìn)而基于密度聚類思想對二維數(shù)據(jù)點(diǎn)進(jìn)行聚類;3)對密度聚類后的規(guī)則種群進(jìn)行適當(dāng)聚合,生成最終的規(guī)則種群。在第一個(gè)過程中,采用學(xué)習(xí)分類系統(tǒng)框架生成規(guī)則種群并進(jìn)行適當(dāng)約減。第二個(gè)過程認(rèn)為種群的各規(guī)則簇中心比它們的鄰居規(guī)則具有更高的密度,并且與密度更高的規(guī)則間距離更大。在第三個(gè)過程中,采用圖分割方法對相關(guān)重疊簇進(jìn)行適當(dāng)聚合。在實(shí)驗(yàn)中,將所提方法與K-means、近鄰傳播聚類算法(AP)、Voting-XCSc等算法進(jìn)行了比較,實(shí)驗(yàn)結(jié)果表明,所提方法在精度方面優(yōu)于對比算法。
[Abstract]:A density clustering method based on complex learning classification system (XCS) is proposed, which can be used for clustering analysis of two dimensional data with arbitrary shape and noise. This method, called DXCSc, mainly consists of the following three processes: 1) based on the learning classification system, the rule population is generated for the input data, and the rules are appropriately compressed) the rules that have been generated are regarded as two-dimensional data points. Then, based on the idea of density clustering, the two-dimensional data points are clustered by 3) the regular population after density clustering is properly aggregated, and the final regular population is generated. In the first process, the learning classification system framework is used to generate the rule population and reduce the rule population. The second process considers that the regular cluster centers of the population have higher density than their neighbor rules, and the distance between the regular cluster centers and the higher density rules is larger. In the third process, the graph segmentation method is used to polymerize the overlapped clusters. In the experiment, the proposed method is compared with K-means, nearest neighbor propagation clustering algorithm and Voting-XCSc algorithm. The experimental results show that the proposed method is superior to the contrast algorithm in accuracy.
【作者單位】： 計(jì)算機(jī)軟件新技術(shù)國家重點(diǎn)實(shí)驗(yàn)室(南京大學(xué));江蘇省審計(jì)廳;
【基金】：江蘇省重點(diǎn)研發(fā)計(jì)劃(產(chǎn)業(yè)前瞻與共性關(guān)鍵技術(shù))項(xiàng)目(BE2015213)~~
【分類號】：TP181

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