Explanation Oriented Association Mining Using Rough Set Theory

Yao, Yiyu; Zhao, Yan; Maguire, R. Brien

doi:10.1007/3-540-39205-x_21

Cited by 25 publications

(10 citation statements)

References 13 publications

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“…The entire knowledge discovery process includes data preparation, data selection and reduction, data pre-processing and transformation, pattern discovery, pattern explanation and evaluation, and pattern presentation (Brachmann & Anand, 1996;Fayyad, et al, 1996;Mannila, 1997;Yao, Zhao & Maguire, 2003;Yao, Zhong & Zhao, 2004). In an interactive system, these phases can be carried out as follows:…”

Section: Processes Of Interactive Data Miningmentioning

confidence: 99%

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On Interactive Data Mining

Zhao

Yao

2009

Encyclopedia of Data Warehousing and Mining, Second Edition

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INTRODUCTIONExploring and extracting knowledge from data is one of the fundamental problems in science. Data mining consists of important tasks, such as description, prediction and explanation of data, and applies computer technologies to nontrivial calculations. Computer systems can maintain precise operations under a heavy information load, and also can maintain steady performance. Without the aid of computer systems, it is very difficult for people to be aware of, to extract, to search and to retrieve knowledge in large and separate datasets, let alone interpreting and evaluating data and information that are constantly changing, and then making recommendations or predictions based on inconsistent and/or incomplete data.On the other hand, the implementations and applications of computer systems reflect the requests of human users, and are affected by human judgement, preference and evaluation. Computer systems rely on human users to set goals, to select alternatives if an original approach fails, to participate in unanticipated emergencies and novel situations, and to develop innovations in order to preserve safety, avoid expensive failure, or increase product quality (Elm, et al., 2004;Hancock & Scallen, 1996;Shneiderman, 1998).Users possess varied skills, intelligence, cognitive styles, and levels of tolerance of frustration. They come to a problem with diverse preferences, requirements and background knowledge. Given a set of data, users will see it from different angles, in different aspects, and with different views. Considering these differences, a universally applicable theory or method to serve the needs of all users does not exist. This motivates and justifies the co-existence of numerous theories and methods of data mining systems, as well as the exploration of new theories and methods.According to the above observations, we believe that interactive systems are required for data mining tasks. Generally, interactive data mining is an integration of human factors and artificial intelligence (Maanen, Lindenberg and Neerincx, 2005); an interactive system is an integration of a human user and a computer machine, communicating and exchanging information and knowledge. Through interaction and communication, computers and users can share the tasks involved in order to achieve a good balance of automation and human control. Computers are used to retrieve and keep track of large volumes of data, and to carry out complex mathematical or logical operations. Users can then avoid routine, tedious and error-prone tasks, concentrate on critical decision making and planning, and cope with unexpected situations (Elm, et al., 2004;Shneiderman, 1998). Moreover, interactive data mining can encourage users' learning, improve insight and understanding of the problem to be solved, and stimulate users to explore creative possibilities. Users' feedback can be used to improve the system. The interaction is mutually beneficial, and imposes new coordination demands on both sides.

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Section: Processes Of Interactive Data Miningmentioning

confidence: 99%

“…The subjects selected for explanation, the explanation context, the explanation construction methods, as well as the explanation evaluation methods all highly dependent upon the preference of an individual user. Please refer to another paper (Yao, Zhao & Maguire, 2003) for further details on this topic.…”

Section: Implementation Examplesmentioning

confidence: 99%

On Interactive Data Mining

Zhao

Yao

2009

Encyclopedia of Data Warehousing and Mining, Second Edition

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“…Decision rule mining [16] [12] [23] can be a possible solution to specify association rules. However, there exists ambiguities whist we use the decision rules for determining other relevance information for specified topics.…”

Section: Related Workmentioning

confidence: 99%

Rough Association Rule Mining in Text Documents for Acquiring Web User Information Needs

Zhong

2006

2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06)

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It is a big challenge to apply data mining techniques for effective Web information gathering because of duplications and ambiguities of data values (e.g., terms). To provide an effective solution to this challenge, this paper first explains the relationship between association rules and rough set based decision rules. It proves that a decision pattern is a kind of closed pattern.It also presents a novel concept of rough association rules in order to improve the effectiveness of association rule mining. The premise of a rough association rule consists of a set of terms and a frequency distribution of terms. The distinct advantage of rough association rules is that they contain more specific information than normal association rules. It is also feasible to update rough association rules dynamically to produce effective results.

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“…Rough set theory describes decision rules by decision table, which compresses databases into granules and reveals the associations between granules [3,4]. The advantage of using decision rules is to reduce the two-steps of association mining into one process.…”

Section: Introductionmentioning

confidence: 99%

Discovering Novel Knowledge Using Granule Mining

Liu

Tian

2012

Rough Sets and Current Trends in Computing

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Abstract. This paper presents an extended granule mining based methodology, to effectively describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other granules, it provides a kind of novel knowledge in databases. We also provide an algorithm to implement the proposed methodology. The experiments conducted to characterize a real network traffic data collection show that the proposed concepts and algorithm are promising.

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Explanation Oriented Association Mining Using Rough Set Theory

Cited by 25 publications

References 13 publications

On Interactive Data Mining

On Interactive Data Mining

Rough Association Rule Mining in Text Documents for Acquiring Web User Information Needs

Discovering Novel Knowledge Using Granule Mining

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