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Article type: Cover
2002 Volume 12 Issue 4 Pages
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Article type: Index
2002 Volume 12 Issue 4 Pages
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Article type: Index
2002 Volume 12 Issue 4 Pages
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Takahiko Tanahashi
Article type: Article
2002 Volume 12 Issue 4 Pages
335-
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Article type: Appendix
2002 Volume 12 Issue 4 Pages
336-338
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Jun-ichi Takeuchi
Article type: Article
2002 Volume 12 Issue 4 Pages
339-340
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Kenji Yamanishi
Article type: Article
2002 Volume 12 Issue 4 Pages
341-356
Published: December 25, 2002
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Data and text mining is a science that statistics, machine learning, and data bases technologies are unified into for the purpose of knowledge discovery from a large amount of data. Specifically among the advanced topics in this area we pick up the issues of statistical outluer detection, outlier filtering rule generation, time series mining, and reputation-analysis on the web, and introduce an approach to them from the pointview of information-based induction sciences.
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Naoki Katoh
Article type: Article
2002 Volume 12 Issue 4 Pages
357-365
Published: December 25, 2002
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The rapid development of modern information technologies has led to important progress in the collecting, processing, and dissemination of data. This has resulted in the accumulation of tremendous amount of business data in databases. The ability to leverage the data has become a key success factor in an increasingly competitive global market. Knowledge discovery in databases or data mining is a new technology or methodology that seeks to automatically extract meaningful knowledge from business data. However, it is not a trivial task. Also, in order that data mining techniques can be used in real business, it is crucial to extract meaningful knowledge that can be turned into business action. The purpose of this paper is to demonstrate how useful knowledge can be extracted by new techniques of analyzing string patterns that were originally developed for areas such as genetic sequencing. We shall demonstrate a few experimental results.
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Hiroki Arimura, Hiroshi Sakamoto
Article type: Article
2002 Volume 12 Issue 4 Pages
366-378
Published: December 25, 2002
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Kazumi Saito
Article type: Article
2002 Volume 12 Issue 4 Pages
379-387
Published: December 25, 2002
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As an approach to data mining, we focus on technologies for learning in neural net-works., In this paper, after explaining two discovery algorithms developed for modeling complex phenomena, we describe an approach to improving scientific models that are cast as sets of equations. As an application using these methods, we review one such model for aspects of the Earth ecosystem, then recount its application to revising parameter values, intrinsic properties, and functional forms, in each case achieving reduction in error on Earth science data while retaining the communicability of the original model.
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Yasuhiko Morimoto
Article type: Article
2002 Volume 12 Issue 4 Pages
388-400
Published: December 25, 2002
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We consider the problem of finding neighboring class sets. Objects of each instance of a neighboring class set are grouped using their Euclidean distances from each other. For example, we have a database containing large number of access records of a location-based service. Records of the objects may consist of "requested service name," "number of packet transmitted" in addition to x and y coordinate values indicating where the request came from. The algorithm presented here efficiently finds sets of "service names" that were frequently close to each other in the spatial database. For example, it may find a frequent neighboring class set, where "ticket" and "timetable" are frequently requested close to each other. By recognizing this, location-based service providers can promote a "ticket" service for customers who access the "timetable."
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Atsuyoshi Nakamura
Article type: Article
2002 Volume 12 Issue 4 Pages
401-410
Published: December 25, 2002
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We explain automatic recommendation techniques by which computer systems can automatically select goods or documents preferred by each user based on his/her buying or accessing history. Especially, we focus on the collaborative filtering method using weighted majority prediction algorithm developed by the authors. From both theoretical and experimental points of view, we explain how excellent the method is compared to the method using correlation coefficient, which is the most popular collaborative filtering method. We also address issues that we should consider in order to make our method practically useful.
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Yoh Iwasa
Article type: Article
2002 Volume 12 Issue 4 Pages
411-418
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Suguru Arimoto
Article type: Article
2002 Volume 12 Issue 4 Pages
419-423
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Takashi Tsuchiya
Article type: Article
2002 Volume 12 Issue 4 Pages
424-425
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Hiroshi Matsuzoe
Article type: Article
2002 Volume 12 Issue 4 Pages
425-426
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Masato Kimura
Article type: Article
2002 Volume 12 Issue 4 Pages
426-
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Manabu Hasegawa
Article type: Article
2002 Volume 12 Issue 4 Pages
427-428
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Yohji Uchiyama
Article type: Article
2002 Volume 12 Issue 4 Pages
428-429
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Article type: Appendix
2002 Volume 12 Issue 4 Pages
430-431
Published: December 25, 2002
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Article type: Appendix
2002 Volume 12 Issue 4 Pages
431-
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Article type: Appendix
2002 Volume 12 Issue 4 Pages
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Article type: Appendix
2002 Volume 12 Issue 4 Pages
432-433
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Article type: Appendix
2002 Volume 12 Issue 4 Pages
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Article type: Index
2002 Volume 12 Issue 4 Pages
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Article type: Appendix
2002 Volume 12 Issue 4 Pages
436-
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Article type: Cover
2002 Volume 12 Issue 4 Pages
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Published: December 25, 2002
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