Mining Generalized Closed Frequent Itemsets of Generalized Association Rules

Sriphaew, Kritsada; Theeramunkong, Thanaruk

doi:10.1007/978-3-540-45224-9_66

Cited by 19 publications

(27 citation statements)

References 11 publications

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“…Thus, we need to prune the search space in order to reduce the number of sequences to be checked and need an efficient implementation for checking supports. We adapt and combine constraints in [1] and in [29,30] for semi-continuous generalized motifs, and we extend optimization ideas in [3,4] for generalized semi-continuous motifs.…”

Section: Discovery Of Generalized Semi-continuous Motifsmentioning

confidence: 99%

Motif-Based Classification of Time Series with Bayesian Networks and SVMs

Búza

Schmidt-Thieme²

2009

Advances in Data Analysis, Data Handling and Business Intelligence

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Summary. Classification of time series is an important task with many challenging applications like brain wave (EEG) analysis, signature verification or speech recognition. In this paper we show how characteristic local patterns (motifs) can improve the classification accuracy. We introduce a new motif class, generalized semi-continuous motifs. To allow flexibility and noise robustness, these motifs may include gaps of various lengths, generic and more specific wildcards. We propose an efficient algorithm for mining generalized sequential motifs. In experiments on real medical data, we show how generalized semi-continuous motifs improve the accuracy of SVMs and Bayesian Networks for time series classificiation.

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Section: Discovery Of Generalized Semi-continuous Motifsmentioning

confidence: 99%

Motif-Based Classification of Time Series with Bayesian Networks and SVMs

Búza

Schmidt-Thieme²

2009

Advances in Data Analysis, Data Handling and Business Intelligence

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“…There exists an alternative compact representation of frequent g-itemsets: the closed frequent gitemsets [6] . The representation of closed frequent gitemsets has the following goal: from this compact representation, one should be able to derive not only the set of all frequent g-itemsets, but also to rapidly retrieve the exact support of each frequent g-itemset.…”

Section: How Are Closed Frequent G-itemsets Andmentioning

confidence: 99%

“…The idea was to utilize the vertical database format (for every item, store the IDs of transactions that involve the item). Sriphaew and Theeramunkong [4,5] proposed the SET algorithm to mine frequent g-itemsets.…”

Section: Mining Generalized Itemsetsmentioning

confidence: 99%

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Mining Frequent Generalized Itemsets and Generalized Association Rules Without Redundancy

Kunkle

Zhang

Cooperman

2008

J. Comput. Sci. Technol.

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This paper presents some new algorithms to efficiently mine max frequent generalized itemsets (g-itemsets) and essential generalized association rules (g-rules). These are compact and general representations for all frequent patterns and all strong association rules in the generalized environment. Our results fill an important gap among algorithms for frequent patterns and association rules by combining two concepts. First, generalized itemsets employ a taxonomy of items, rather than a flat list of items. This produces more natural frequent itemsets and associations such as (meat, milk) instead of (beef, milk), (chicken, milk), etc. Second, compact representations of frequent itemsets and strong rules, whose result size is exponentially smaller, can solve a standard dilemma in mining patterns: with small threshold values for support and confidence, the user is overwhelmed by the extraordinary number of identified patterns and associations; but with large threshold values, some interesting patterns and associations fail to be identified.Our algorithms can also expand those max frequent g-itemsets and essential g-rules into the much larger set of ordinary frequent g-itemsets and strong g-rules. While that expansion is not recommended in most practical cases, we do so in order to present a comparison with existing algorithms that only handle ordinary frequent g-itemsets. In this case, the new algorithm is shown to be thousands, and in some cases millions, of the time faster than previous algorithms. Further, the new algorithm succeeds in analyzing deeper taxonomies, with the depths of seven or more. Experimental results for previous algorithms limited themselves to taxonomies with depth at most three or four.In each of the two problems, a straightforward lattice-based approach is briefly discussed and then a classificationbased algorithm is developed. In particular, the two classification-based algorithms are MFGI class for mining max frequent g-itemsets and EGR class for mining essential g-rules. The classification-based algorithms are featured with conceptual classification trees and dynamic generation and pruning algorithms.

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“…Mining generalized frequent patterns is a well-motivated existing problem [2,3]. Here, generalized itemsets (or patterns) employ a taxonomy of items, rather than a flat list of items.…”

Section: Introductionmentioning

confidence: 99%