著者

Shin-ichi Minato Kimihito Ito

出版者

The Japanese Society for Artificial Intelligence

雑誌

Transactions of the Japanese Society for Artificial Intelligence (ISSN:13460714)

巻号頁・発行日

vol.22, no.2, pp.156-164, 2007 (Released:2007-01-25)

参考文献数

17

被引用文献数

2 4

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In this paper, we present a method of finding symmetric items in a combinatorial item set database. The techniques for finding symmetric variables in Boolean functions have been studied for long time in the area of VLSI logic design, and the BDD (Binary Decision Diagram) -based methods are presented to solve such a problem. Recently, we have developed an efficient method for handling databases using ZBDDs (Zero-suppressed BDDs), a particular type of BDDs. In our ZBDD-based data structure, the symmetric item sets can be found efficiently as well as for Boolean functions. We implemented the program of symmetric item set mining, and applied it to actual biological data on the amino acid sequences of influenza viruses. We found a number of symmetric items from the database, some of which indicate interesting relationships in the amino acid mutation patterns. The result shows that our method is helpful for extracting hidden interesting information in real-life databases.

著者

尾崎 知伸 渡沼 智己 大川 剛直

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.22, no.2, pp.173-182, 2007 (Released:2007-01-25)

参考文献数

43

被引用文献数

1

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Recently, the research area of mining in structured data has been actively studied. However, since most techniques for structured data mining so far specialize in mining from single structured data, it is difficult for these techniques to handle more realistic data which is related to various types of attribute and which consists of plural kinds of structured data. Since such kind of data is expected to be going to rapidly increase, we need to establish a flexible and highly accurate technique that can inclusively treat such kind of data. In this paper, as one of the techniques to deal with such kind of data, we propose data mining algorithms of mining classification rules in multidimensional structured data. First, an algorithm with two pruning capabilities of mining correlated patterns is introduced. Then, top-k multidimensional correlated patterns are discovered by using this algorithm repeatedly in the fashion like a beam search. We also show the algorithms for constructing classifiers based on the discovered patterns. Experiments with real world data were conducted to assess the effectiveness of the proposed algorithms. The results show that the proposed algorithms can construct comprehensible and accurate classifiers within a reasonable running time.

著者

Shin-ichi Minato Hiroki Arimura

出版者

The Japanese Society for Artificial Intelligence

雑誌

Transactions of the Japanese Society for Artificial Intelligence (ISSN:13460714)

巻号頁・発行日

vol.22, no.2, pp.165-172, 2007 (Released:2007-01-25)

参考文献数

11

被引用文献数

1 5 9

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Frequent item set mining is one of the fundamental techniques for knowledge discovery and data mining. In the last decade, a number of efficient algorithms for frequent item set mining have been presented, but most of them focused on just enumerating the item set patterns which satisfy the given conditions, and it was a different matter how to store and index the result of patterns for efficient data analysis. Recently, we proposed a fast algorithm of extracting all frequent item set patterns from transaction databases and simultaneously indexing the result of huge patterns using Zero-suppressed BDDs (ZBDDs). That method, ZBDD-growth, is not only enumerating/listing the patterns efficiently, but also indexing the output data compactly on the memory to be analyzed with various algebraic operations. In this paper, we present a variation of ZBDD-growth algorithm to generate frequent closed item sets. This is a quite simple modification of ZBDD-growth, and additional computation cost is relatively small compared with the original algorithm for generating all patterns. Our method can conveniently be utilized in the environment of ZBDD-based pattern indexing.

著者

Nozomi Kobayashi Kentaro Inui Yuji Matsumoto

出版者

The Japanese Society for Artificial Intelligence

雑誌

Transactions of the Japanese Society for Artificial Intelligence (ISSN:13460714)

巻号頁・発行日

vol.22, no.2, pp.227-238, 2007 (Released:2007-01-25)

参考文献数

19

被引用文献数

3 17 38

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The task of opinion extraction and structurization is the key component of opinion mining, which allow Web users to retrieve and summarize people's opinions scattered over the Internet. Our aim is to develop a method for extracting opinions that represent evaluation of concumer products in a structured form. To achieve the goal, we need to consider some issues that are relevant to the extraction task: How the task of opinion extraction and structurization should be designed, and how to extract the opinions which we defined. We define an opinion unit consisting of a quadruple, that is, the opinion holder, the subject being evaluated, the part or the attribute in which it is evaluated, and the evaluation that expresses positive or negative assessment. In this task, we focus on two subtasks (a) extracting subject/aspect-evaluation relations, and (b) extracting subject/aspect-aspect relations, we approach each extraction task using a machine learning-based method. In this paper, we discuss how customer reviews in web documents can be best structured. We also report on the results of our experiments and discuss future directions.

著者

幸島 明男 和泉 憲明 車谷 浩一 中島 秀之

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.19, no.4, pp.322-333, 2004 (Released:2004-05-28)

参考文献数

34

被引用文献数

2

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In the vision of the semantic web, agents are defined as the programs that collect information from diverse sources, process the information, and exchange the results with other programs. In order to extend application areas of the agents from the Internet to the real world, we propose CONSORTS, a multiagent architecture for content managements in ubiquitous computing. In this paper, we, first, describe two important concepts in order to realize the agent-based content managements in ubiquitous computing, ``physically grounding'' and ``cognitive resources managements.'' Second, we describe the outline of the CONSORTS (ver. 1) and its RDF-based spatial information representation. Finally, we show an application of the CONSORTS, context-aware information assist services in a museum.

著者

原田 実 水野 高宏

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.16, no.1, pp.85-93, 2001 (Released:2002-02-28)

参考文献数

15

被引用文献数

3 6

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Up to now, the research on the automation of object-oriented analysis, especially extracting objectoriented design elements from the problem specification written in Japanese, has been continued in the Harada laboratory since 1993. As this first process, we have developed the semantic analysis system SAGE which could be practically useable both in the performance and in the accuracy. Given a dependency tree, where clauses constituting a sentence are related by dependency arcs, SAGE searches the EDR electronic dictionary, retrieves for any two clauses connected by a dependency arc the meaning of the principal word in each clause and the deep case between such two words, and assigns the probability of such meaning-case tuple. Then, SAGE constructs an interpretation tree by allocating such meaning-case tuple and its probability to each arc in the dependency tree. Next, SAGE searches for the allocation having the maximum of the overall evaluation value given by the sum of the probability of the allocated meaning and cases. Finally, SAGE converts the resulting interpretation tree into the set of semantic frames containing the information of each word and relations with other words. In developing the system, we achieved speed-up of the construction of the interpretation tree by reducing the search space with pruning useless meaning-case tuples and by using the branch and bound method. Moreover the accuracy improvement of the analysis was achieved by applying the following four methods: (A)in constructing the interpretation tree, assigning 0 probability to all the combination of word meanings with which there are no “case” information in the concept description dictionary, (B)using the experimental rules to presume the deep cases from the surface cases to each dependency between verb clauses, (C)improving the fitness of the sentences retrieved from corpus by using part of speech, and (D)decreasing the number of meaning candidates by using reading information. As a result, the average interpretation construction time of one sentence with nine clauses or less was 2 seconds on a PC with the Pentium III processor using 320MB memory. The correct answer rate of the meaning was 82.1%, and that of the case was 77.8%.