01249nas a2200205   4500008004100000245006800041210006400109260000900173300001400182490000700196520059500203653001400798653001600812653002500828653002700853653002200880100001300902700001100915856011700926       2013                        eng d00aThe research of algorithm for data mining based on fuzzy theory0 aresearch of algorithm for data mining based on fuzzy theory  c2013  a327 - 3340 v113 aData Mining is a new filed in data processing research. Support Vector Machine (SVM) is one of the new methods using in data mining, which has gained great applicable success. However, there are still plenty of limitations in SVM. For example, SVM won't work if its training set contains uncertain information. In order to solve the problem presented above, this paper discusses the constraining programming of fuzzy chance and the characteristic of fuzzy classification as well as its expression methods. The algorithm for classifying Support Vector Machine is also included in this paper.10aAlgorithm10aData mining10aFuzzy classification10aFuzzy linear separable10aFuzzy programming1 aWang, A.1 aLi, J.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84890455119&partnerID=40&md5=cafd40536136512fd2e0485a0240a56501890nas a2200193   4500008004100000245007900041210006900120260000900189300001400198490000700212520125000219653001401469653002001483653002001503653002601523100001601549700001401565856011701579       2012                        eng d00aDistributed weighted clustering of evolving sensor data streams with noise0 aDistributed weighted clustering of evolving sensor data streams   c2012  a410 - 4200 v103 aCollecting data from sensor nodes is the ultimate goal of Wireless Sensor Networks. This is performed by transmitting the sensed measurements to some data collecting station. In sensor nodes, radio communication is the dominating consumer of the energy resources which are usually limited. Summarizing the sensed data internally on sensor nodes and sending only the summaries will considerably save energy. Clustering is an established data mining technique for grouping objects based on similarity. For sensor networks, k-center clustering aims at grouping sensor measurements in groups, each contains similar measurements. In this paper we propose a novel resource-aware -center clustering algorithm called: SenClu. Our algorithm immediately detects new trends in the drifting sensor data stream and follows them. SenClu powerfully uses a lightweighted decaying technique that gives lower influence to old data. As sensor data are usually noisy, our algorithm is also outlier-aware. In thorough experiments on drifting synthetic and real world data sets, we show that SenClu outperforms two state-of-the-art algorithms by producing higher clustering quality and following trends in the stream, while consuming nearly the same amount of energy.10aAlgorithm10aData clustering10aData Processing10aWireless Sensor Nodes1 aHassani, M.1 aSeidl, T.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84874869330&partnerID=40&md5=29927101aa08b9c82193c09bb9c50304