Advanced Data Mining and Applications: 6th International by Qiang Li Zhao, Yan Huang Jiang, Ming Xu (auth.), Longbing

By Qiang Li Zhao, Yan Huang Jiang, Ming Xu (auth.), Longbing Cao, Jiang Zhong, Yong Feng (eds.)

With the ever-growing energy of producing, transmitting, and gathering large quantities of knowledge, info overloadis nowan coming near near problemto mankind. the overpowering call for for info processing isn't just a few higher figuring out of knowledge, but additionally a greater utilization of knowledge promptly. facts mining, or wisdom discovery from databases, is proposed to achieve perception into features ofdata and to assist peoplemakeinformed,sensible,and larger judgements. at this time, growing to be realization has been paid to the research, improvement, and alertness of information mining. accordingly there's an pressing desire for classy ideas and toolsthat can deal with new ?elds of knowledge mining, e. g. , spatialdata mining, biomedical info mining, and mining on high-speed and time-variant info streams. the data of information mining also needs to be elevated to new functions. The sixth overseas convention on complex information Mining and Appli- tions(ADMA2010)aimedtobringtogethertheexpertsondataminingthrou- out the area. It supplied a number one foreign discussion board for the dissemination of unique study leads to complicated information mining thoughts, purposes, al- rithms, software program and structures, and di?erent utilized disciplines. The convention attracted 361 on-line submissions from 34 di?erent international locations and parts. All complete papers have been peer reviewed through at the least 3 participants of this system Comm- tee composed of overseas specialists in facts mining ?elds. a complete variety of 118 papers have been permitted for the convention. among them, sixty three papers have been chosen as average papers and fifty five papers have been chosen as brief papers.

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Extra info for Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part II

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DA. Example 4 (Similarity between tuples). Consider the feature vectors on attribute Type in Fig. 2. The similarity between t1 and t2 on aggregate features TypeCOUNT, TypeCOUNT UNIQUE, and TypeFREQUENT are all 1. On the distribution feature DType in Fig. 45. Definition 3 is important since it gives us a ground to compare how features, either aggregate or distribution ones, manifest the similarity between target tuples. For a feature Af, we can calculate the similarity simAf(t1,t2) for each pair of target tuples (t1, t2).

Springer-Verlag Berlin Heidelberg 2010 22 M. Zou et al. There are many mature and effective classification methods for data in a single relation, such as SVM and decision trees [2]. Those methods cannot be applied directly for multi-relational classification since they cannot handle multiple relations and their connections. Thus, a natural question is whether we can derive a general feature generation and selection method to build the feature space from multiple relations, so that those existing classification methods for single relations can be easily extended to multi-relational classification.

To handle the efficiency issue of ILP systems, CrossMine [11] develops a tuple ID propagation method to avoid physically joining relations. Alternative to the inductive logic programming approaches, a possible idea is to transform a multi-relational data set into a “flattened” universal relation. The propositionalization approaches use inductive logic programming to flatten multi-relational data and generate features [12]. Particularly, the features in the universal relation are generated by first-order clauses using inductive logic programming.

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