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Authors: Hiroyuki Morita and Mao Nishiguchi

Affiliation: Osaka Prefecture University, Japan

ISBN: 978-989-8565-59-4

ISSN: 2184-4992

Keyword(s): Contrast Pattern, Emerging Pattern, Classification Model.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: A frequent pattern that occurs in a database can be an interesting explanatory variable. For instance, in market basket analysis, a frequent pattern is used as an association rule for historical purchasing data. Moreover, specific frequent patterns as emerging patterns and contrast patterns are a promising way to estimate classes in a classification problem. A classification model using the emerging patterns, Classification by Aggregating Emerging Patterns(CAEP) has been proposed (Dong et al., 1999) and several applications have been reported. It is a simple and effective method, but for some practical data, it can be computationally costs to enumerate large emerging patterns or may cause unpredicted cases. We think that there are two major reasons for this. One is emerging patterns, which are powerful when constructing a predictive model; however, they are not able to cover frequent transactions. Because of this, some of the transactions are not estimated, and the accuracy of the estimat ion becomes poor. Another reason is the normalization method. In CAEP, scores for each class are normalized by dividing by the median. It is a simple method, but the score distribution is sometimes biased. Instead, we propose the use of the z − score for normalization. In this paper, we propose a new, CAEP-based classification model, Classification by Aggregating Contrast Patterns (CACP). The main idea is to use contrast patterns instead of emerging patterns and to improve the normalizing method. Our computational experiments show that our method, CACP, performs better than the existing CAEP method on real data. (More)

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Paper citation in several formats:
Morita, H. and Nishiguchi, M. (2013). Classification Model using Contrast Patterns.In Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 3: ICEIS, ISBN 978-989-8565-59-4, ISSN 2184-4992, pages 334-339. DOI: 10.5220/0004569403340339

author={Hiroyuki Morita. and Mao Nishiguchi.},
title={Classification Model using Contrast Patterns},
booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 3: ICEIS,},


JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 3: ICEIS,
TI - Classification Model using Contrast Patterns
SN - 978-989-8565-59-4
AU - Morita, H.
AU - Nishiguchi, M.
PY - 2013
SP - 334
EP - 339
DO - 10.5220/0004569403340339

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