Mechanizing Hypothesis Formation

Hájek, Petr; Havránek, Tomáš

doi:10.1007/978-3-642-66943-9

Cited by 173 publications

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“…Motivated by this result, we generalise two inequalities due to Hájek and Havránek [6], and show that there are more related hypergeometric tails, p(. ), satisfying (1).…”

Section: Introductionmentioning

confidence: 81%

“…A special inequality between the tail probabilities of certain related hypergeometrics was shown by Seneta and Phipps [19] to suggest useful 'quasi-exact' alternatives to Fisher's [5] Exact Test. With this result as motivation, two inequalities of Hájek and Havránek [6] are investigated in this paper and are generalised to produce inequalities in the form required. A parallel inequality in binomial tail probabilities is also established.…”

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confidence: 99%

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Inequalities between hypergeometric tails

Phipps

2003

Journal of Applied Mathematics and Decision Sciences

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Abstract. A special inequality between the tail probabilities of certain related hypergeometrics was shown by Seneta and Phipps [19] to suggest useful 'quasi-exact' alternatives to Fisher's [5] Exact Test. With this result as motivation, two inequalities of Hájek and Havránek [6] are investigated in this paper and are generalised to produce inequalities in the form required. A parallel inequality in binomial tail probabilities is also established.

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“…Motivated by this result, we generalise two inequalities due to Hájek and Havránek [6], and show that there are more related hypergeometric tails, p(. ), satisfying (1).…”

Section: Introductionmentioning

confidence: 81%

mentioning

confidence: 99%

Inequalities between hypergeometric tails

Phipps

2003

Journal of Applied Mathematics and Decision Sciences

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“…Even a mere innovative approach to data visualization, for example, can help to see the benefits of the given data from a completely new perspective. The GUHA method, introduced in 1966 (see [2]), is one of the oldest data mining methods. GUHA is an abbreviation for General Unary Hypoteses Automation.…”

Section: Introductionmentioning

confidence: 99%

Using GUHA Data Mining Method in Analyzing Road Traffic Accidents Occurred in the Years 2004–2008 in Finland

Turunen

2017

Data Sci. Eng.

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The suitability of the GUHA data mining method in analyzing a big data matrix is studied in this report in general, and, in particular, a data matrix containing more than 80,000 road traffic accidents occurred in Finland in 2004-2008 is examined by LISp-Miner, a software implementation of GUHA. The general principles of GUHA are first outlined, and then, the road accident data is analyzed. As a result, more than 10,000 associations and dependencies, called hypothesis in the GUHA language, were found; some easily understandable of them are presented here. Our conclusion is that the GUHA method is useful, in particular when one wants to explore relatively small size, but still significant dependencies in a given large data matrix.

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confidence: 96%

“…Its principle was formulated long before the advent of data mining. Its theoretical foundations (as presented in [5] and later publications) lead to the theory described in points (1), (2) above.(4) Finally, to show how modern fuzzy logic (in the narrow sense of the word, i.e. particular many-valued symbolic logic) may enter the domain of KDD and fruitfully generalize the field.…”

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confidence: 99%

Logics and Statistics for Association Rules and Beyond

Hájek

Rauch

1999

Principles of Data Mining and Knowledge Discovery

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The aim of the tutorial is four-fold: (1) To present a very natural class of logical systems suitable for formalizing, generating and evaluating statements on dependences found in given data. The popular association rules form a particular, but by far not the only example. Technically, our logical systems are monadic observational predicate calculi, i.e. calculi with generalized quantifiers, only finite models and effective semantics. Be not shocked by these terms; no non-trivial knowledge of predicate logic will be assumed. Logic includes deductive properties, i.e. possibility to deduce truth of a sentence from other sentences already found true. Transparent deduction rules are very useful for systematic data mining. Special attention will be paid to sentences formalizing expressions of the form "many objects having a certain combination A of attributes have a also combination B" and, more generally, "combinations A, B of attributes are associated (dependent, correlated etc.) in a precisely defined manner".(2) To show how suitable observational sentences are related to statistical hypothesis testing (this aspect appears sometimes unjustly neglected in data mining). A general pattern of statistical inference will be presented in logical terms (theoretical calculi and their relation to observational ones). In particular, statistical meaning of two variants of "associational rules" as well as of some "symmetric associations" will be explained.(3) To present short history of the GUHA method of automatic generation of hypotheses (General Unary Hypothesis Automaton). It is an original Czech method of exploratory data analysis, one of the oldest approaches to KDD or data mining. Its principle was formulated long before the advent of data mining. Its theoretical foundations (as presented in [5] and later publications) lead to the theory described in points (1), (2) above.(4) Finally, to show how modern fuzzy logic (in the narrow sense of the word, i.e. particular many-valued symbolic logic) may enter the domain of KDD and fruitfully generalize the field. This fourth point will concentrate to open problems and research directions.The tutorial will be complemented by demonstrations of two recent implementations of the GUHA method.

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Mechanizing Hypothesis Formation

Cited by 173 publications

References 0 publications

Inequalities between hypergeometric tails

Inequalities between hypergeometric tails

Using GUHA Data Mining Method in Analyzing Road Traffic Accidents Occurred in the Years 2004–2008 in Finland

Logics and Statistics for Association Rules and Beyond

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