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Titlebook: New Frontiers in Applied Data Mining; PAKDD 2008 Internati Sanjay Chawla,Takashi Washio,Akihiro Inokuchi Conference proceedings 2009 Spring

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書目名稱New Frontiers in Applied Data Mining
副標(biāo)題PAKDD 2008 Internati
編輯Sanjay Chawla,Takashi Washio,Akihiro Inokuchi
視頻videohttp://file.papertrans.cn/666/665234/665234.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: New Frontiers in Applied Data Mining; PAKDD 2008 Internati Sanjay Chawla,Takashi Washio,Akihiro Inokuchi Conference proceedings 2009 Spring
描述This book constitutes the proceedings of the PAKDD Workshops 2008, namely ALSIP 2008, DMDRM 2008, and IDM 2008. The workshops were held in conjunction with the PAKDD conference in Osaka, Japan, during May 20-23, 2008. The 17 papers presented were carefully reviewed and selected from 38 submissions. The International Workshop on Algorithms for Large-Sale Information Processing in Knowledge Discovery (ALSIP) focused on exchanging fresh ideas on large-scale data processing in the problems of data mining, clustering, machine learning, statistical analysis, and other computational aspects of knowledge discovery problems. The Workshop on Data Mining for Decision Making and Risk Management (DMDRM) covered data mining and machine learning approaches, statistical approaches, chance discovery, active mining and application of these techniques to medicine, marketing, security, decision support in business, social activities, human relationships, chemistry and sensor data. The Workshop on Interactive Data Mining Overview (IDM) discussed various interactive data mining researches such as interactive information retrieval, information gathering sysetms, personalization systems, recommendation sy
出版日期Conference proceedings 2009
關(guān)鍵詞ALS; Clustering; algorithms; data mining; information processing; knowledge; knowledge discovery; learning;
版次1
doihttps://doi.org/10.1007/978-3-642-00399-8
isbn_softcover978-3-642-00398-1
isbn_ebook978-3-642-00399-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2009
The information of publication is updating

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Incrementally Mining Recently Repeating Patterns over Data Streamsf various applications is generated as a data stream. Based on time sensitive concern, mining repeating patterns from the whole history data sequence of a data stream does not extract the current trend of patterns over the stream. Therefore, the traditional strategies for mining repeating patterns o
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A Framework for Mining Fuzzy Association Rules from Composite Items each feature a number of values derived from a common schema. To apply fuzzy Association Rule Mining (ARM) we partition the property values into fuzzy property sets. This paper describes: (i) the process of deriving the fuzzy sets (Composite Fuzzy ARM or CFARM) and (ii) a unique property ARM algori
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Sibling Distance for Rooted Labeled Treesbel of a node and the sequence of labels of its children. Then, we show that .. gives a . on the tree edit distance . such that ..(..,..)?≤?4.(..,..). Next, we design the algorithm to compute the sibling histogram in .(.) time for . trees and in .(.) time for . trees, where . and . are the number of
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