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(31 citation statements)

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“…More precisely, the patterns the LISp-Miner deals with contain several derived Boolean attributes (from two in the 4ft-Miner to five in the SD4ft-Miner). Moreover, the procedures described in Section 14.9 in [6] deal also with categorical attributes not transformed into Boolean ones. However, we have not used these latter procedures in this study.…”

confidence: 99%

“…More precisely, the patterns the LISp-Miner deals with contain several derived Boolean attributes (from two in the 4ft-Miner to five in the SD4ft-Miner). Moreover, the procedures described in Section 14.9 in [6] deal also with categorical attributes not transformed into Boolean ones. However, we have not used these latter procedures in this study.…”

confidence: 99%

“…At present there are 21 quantifiers implemented to LISpMiner [6]. In general, there are various types of generalized quantifiers in GUHA theory formalizing various kinds of associations:…”

confidence: 99%

“…O NE of the main directions in data mining is extracting various kinds of logical rules, such as classification rules, association rules or fuzzy rules, from the available data [1]- [4]. Logical rules, especially the Boolean IF .…”

confidence: 99%

“…During the last 10-15 years, however, quantifiers based on other data analysis methods emerged, in particular, general contingency tables, estimates of joint distributions of multivariate discrete variables, searching for exceptionally high values of some interestingness measures, checking a state change of an object by time series analysis of its attributes (action rules), and fuzzy hypotheses testing [7]- [12]. A survey can be found in [13], whereas much of the underlying theoretical developments of observational calculus has been presented in the recent monograph [4].…”

confidence: 99%

“…Examples of 4ft-quantifiers and their associated functions F ≈ are given in section 2.3. There are about forty 4ft-quantifiers defined and studied in [23]. Associated functions of some of them are defined such that suitable thresholds are used for various measures of interestingness of association rules as defined e.g.…”

mentioning

confidence: 99%