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Collective data mining: a new perspective toward distributed data mining (1999).
| Content Provider | CiteSeerX |
|---|---|
| Author | Park, Byung-Hoon Hershberger, Daryl Johnson, Erik Kargupta, Hillol Chan, Philip Press, Aaai |
| Abstract | This paper introduces the collective data mining (CDM), a new approach toward distributed data mining (DDM) from heterogeneous sites. It points out that naive approaches to distributed data analysis in a heterogeneous environment may face ambiguous situation and may lead to incorrect global data model. It also observes that any function can be expressed in a distributed fashion using a set of appropriate basis functions and orthonormal basis functions can be effectively used for developing a general framework for DDM that guarantees correct local analysis, resulting in desired global data model using minimal data communication. The paper develops the foundation of CDM, discusses decision tree learning and polynomial regression in CDM for discrete and continuous variables, and describes the BODHI, a CDM based experimental system. 1 Introduction Distributed data mining (DDM) is a fast growing area that deals with the problem of finding data patterns in an environment with distributed da... |
| File Format | |
| Publisher Date | 1999-01-01 |
| Access Restriction | Open |
| Subject Keyword | Collective Data Mining Global Data Model Heterogeneous Site General Framework Naive Approach Minimal Data Communication Ambiguous Situation Orthonormal Basis Function Introduction Distributed Data Mining Polynomial Regression Decision Tree Learning Correct Local Analysis New Approach Data Analysis Distributed Fashion Data Mining Continuous Variable Data Pattern Experimental System Appropriate Basis Function Heterogeneous Environment |
| Content Type | Text |
| Resource Type | Article |
