Semantic Web Presentation of Analytical Reports from Data Mining - Preliminary Considerations

Mitra, Saayan; Basu, Samik; Kumar, Ratnesh

doi:10.1109/wi.2007.72

Cited by 15 publications

(10 citation statements)

References 13 publications

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“…Thus, the LISp-Miner system incorporates a core data-mining algorithm with greater capabilities than the a-priori algorithm proposed by . For details about alternative approach to mine for patterns with a rich syntax see [Rauch & Šimůnek, 2005b] and [Hájek et. al., 2010].…”

Section: Prerequisitesmentioning

confidence: 99%

“…Not only task parameters but also types of patterns the automated process is looking for could be influenced by previous results and changed during iterations. This text specifies further and in more details the thoughts published in previous works of [Rauch & Šimůnek, 2005a], [Rauch & Šimůnek, 2007], [Rauch & Šimůnek, 2009a] and mainly in the paper where a formal architecture of the automated data-mining process was firstly proposed. The text is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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New Fundamental Technologies in Data Mining

Funatsu¹

2011

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Prof. Kimito Funatsu was born on December 3, 1955 in Japan. He received his Doctor degree (Dr. Sci.; physical organic chemistry) from Kyushu University which is one of the oldest imperial universities (1983) and joined Prof. Shin-ichi Sasaki's group, Toyohashi University of Technology (1984) as research-associate. During researching in Prof. Sasaki's group he worked on chemoinformatics field, i.e., Structure elucidation system CHEMICS, organic synthesis design system AIPHOS, molecular design. In 1990 he took the post of associate-professor. In 2004 he moved to The University of Tokyo as full Professor to continue the same research work. Recently he is trying material design and soft sensor. He is a chair of division of chemical information and computer sciences, Chemical Society of Japan.Dr. Kiyoshi Hasegawa was born on August 18, 1963 in Kanazawa, Japan. He received his Ph.D. from Toyohashi University of Technology (1995) and worked at Biostructure group in Chugai Pharmaceutical Company in Japan. While working in Chugai, he spent three months at Swiss Basel Research Center in Roche for developing new de novo design program (2003). He is now principal scientist in his group and is involved in many SBDD projects. He is also the Roche data mining working group member and the secretary general of Japan Chemical Information Science. His interests are de novo design, Chemogenomics, data mining and web application.

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Section: Prerequisitesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Fundamental Technologies in Data Mining

Funatsu¹

2011

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“…Thus the research project called SEWEBAR was launched [96,100,101]. Its vision is to automatically produce analytical reports from data mining and to share them on the Semantic Web.…”

Section: Research Projects Related To Lisp-minermentioning

confidence: 99%

The GUHA method and its meaning for data mining

Hájek

Holeňa

Rauch

2010

Journal of Computer and System Sciences

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a r t i c l e i n f o a b s t r a c tThe paper presents the history and present state of the GUHA method, its theoretical foundations and its relation and meaning for data mining. A survey of development of the GUHA method"GUHA" is the acronym for General Unary Hypotheses Automaton. The idea of the method is: given data, let the computer generate all (or as much as possible) interesting hypotheses of a given logical form that are supported by the data. This idea was elaborated by M. Chytil and P. Hájek in mid-sixties of the last century, the first paper in English being [16]. The approach was as follows: Data to be processed form a rectangular matrix of zeros and ones, rows corresponding to objects and columns to attributes (properties). Let P 1 , . . . , P n be names of the attributes. For each attribute P i , ¬P i is the name of its negation. An elementary conjunction of length k (1 k n) is a conjunction of k literals in which each predicate occurs at most once, e.g. ¬P 3 , P 1 & ¬P 3 & P 7 ; similarly an elementary disjunction (e.g. P 1 ∨ ¬P 3 ∨ P 7 ). An object satisfies an elementary conjunction if it satisfies all its members; it satisfies an elementary disjunction if it satisfies at least one of its members.Let 0 p 1. A formula A ⇒ p S where A is an elementary conjunction (antecedent) and S is an elementary disjunction (succedent) is true in the data if at least 100p percent of objects satisfying A satisfies S, i.e. a/r p where r is the number of objects satisfying A and a is the number of objects satisfying both A and S. The antecedent A is t-good (where t is a natural number) if at least t objects satisfy it. The version of GUHA described in [16] systematically generates "strongest" true formulas A ⇒ p S with a t-good antecedent, notation: A ⇒ p,t S. (Details omitted; "strongest" refers to a notion of a logical rule of immediate consequence among formulas of our form.) See also [2].The reader easily recognizes similarity with the notion of an "associational rule with support and confidence" introduced by Agrawal [1] about 25 years later: his A and S are elementary conjunctions containing no negation, p is the confidence and support is t/m, where m is the number of all objects in the data. 35 The formulas found by GUHA (i.e. by a computer program implementing it) have the form "almost all objects satisfying the antecedent satisfy the succedent (and the number of objects satisfying the antecedent is not too small)." It is stressed that the found results are formulas true in the data and they are hypotheses from the point of view of a universe from which the data are a sample. The slogan has been "GUHA offers everything interesting" (all hypotheses of the given form true in the data). The first implementation (by I. Havel) worked on a computer MINSK22.In 1968 Hájek (in a paper in Czech) suggested a different version based on the statistical Fisher test. Given A and S (now two elementary conjunctions with no predicates in common), let a, b, c, d be the numbers of objects satisfying A & B,A & ¬B, ¬A & B and ¬...

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“…It means that such analytical reports are natural candidates for Semantic Web. Project SEWEBAR concerning these ideas is described in (Rauch andŠimůnek, 2007). It is assumed there are various institutions (e.g.…”

Section: Ft-discoverer and Semantic Webmentioning

confidence: 99%

LOGIC OF DISCOVERY, DATA MINING AND SEMANTIC WEB - Position Paper

Rauch¹

2010

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

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Logic of discovery was developed in 1970's as an answer to questions "Can computers formulate and justify scientific hypotheses?" and "Can they comprehend empirical data and process it rationally, using the apparatus of modern mathematical logic and statistics to try to produce a rational image of the observed empirical world?". Logic of discovery is based on two semantic systems. Observational semantic system corresponds to observational data and statements on observational data. Theoretical semantic system concerns suitable state dependent structures. Both systems are related via inductive inference rules corresponding to statistical approaches. An attempt to modify logic of discovery to data mining was made and a framework making possible to deal with domain knowledge in data mining was developed. Possibility of enhancement of this framework for presenting results of data mining through Semantic web is suggested and discussed.

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Semantic Web Presentation of Analytical Reports from Data Mining - Preliminary Considerations

Cited by 15 publications

References 13 publications

New Fundamental Technologies in Data Mining

New Fundamental Technologies in Data Mining

The GUHA method and its meaning for data mining

LOGIC OF DISCOVERY, DATA MINING AND SEMANTIC WEB - Position Paper

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