Ryosuke Fujioka

Fuzzy association rules mining algorithm based on equivalence redundancy of items

In data mining approaches, quantitative attributes should be appropriately dealt with as well as Boolean attributes. This paper presents an essential improvement for extracting fuzzy association rules from a database. The objective of this paper is to improve the computational time of mining and to prune extracted redundant rules simultaneously for an actual data mining application. In this paper, we define the equivalence redundancy of fuzzy items and related theorems as a new concept for fuzzy data mining. Then, we propose a basic algorithm based on the Apriori algorithm for rule extraction utilizing the equivalence redundancy of the fuzzy items based on redundancy concepts of fuzzy association rules. The essential performance of the algorithm is evaluated through numerical experiments using benchmark data. From the results, the method is found to be promising in terms of computational time and redundant-rule pruning.

Improvement of Collaborative Filtering Based on Fuzzy Reasoning Model

Though various contents are provided through the internet recently, it is not easy to collect favorite contents among huge amounts of contents in terms of users preference. In this paper, we focus on the collaborative filtering algorithm in the recommender system. We propose a fuzzy modeling approach for preference similarity model in collaborative filtering. In our approach, valid simplified fuzzy model is constructed through optimization of MAE(Mean Absolute Error). The model decides the weights of preference similarity from the value of correlation coefficient and the number of items. Through numerical experiments compared with conventional correlation coefficient based approach using Movie Lens data, the approach is found to be promising for improvement of collaborative filtering model accuracy.

Preference representation in a content-oriented information retrieval system for local assembly minutes

This paper presents a content-oriented information retrieval method for the local assembly minutes written in Japanese. In this work, the “information retrieval” means the sentence extraction based on our preference specification. In contrast to the conventional keyword-based approach, our discussion puts emphasis on the enrichment of the preference representation capability. We represent preference as a pattern on the syntactic and semantic features. Three preference representation methods are described: One is the rule-based method that requires the direct coding of the preference in the form of the constraints on the syntactic and semantic features. Though this approach enables us to specify the preference in a specification, more intuitive and simpler methods are desired from the viewpoint of application. In order to achieve this, we extend the rule-based method to the example-based and the unification-based methods. Both methods are simple in that preference is specified by example sentence presentation. In the unification-based method, part of the sentences can be left unspecified so that the search engine can fill the appropriate word in the course of search. Illustrative examples are also shown using the actual minutes texts.