Mining top-k regular-frequent itemsets using database partitioning and support estimation

Amphawan, Komate; Lenca, Philippe; Surarerks, Athasit

doi:10.1016/j.eswa.2011.08.055

Cited by 16 publications

(6 citation statements)

References 12 publications

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“…The setting of the thresholds was based on the density of the data in each dataset. However, it was similar to previous approaches ( [5,6,7,30,31,32]). MHUIRA with UL and NUL and HUI-Miner-reg were implemented in and run on Xeon® 2.4 GHz with 64 GB of memory.…”

Section: Methodsmentioning

confidence: 75%

“…To avoid difficulties in setting the support threshold, Amphawan [30] introduced the task of top-frequent-regular itemset mining. A partition and estimation technique was proposed to increase the efficiency of this task [31]. Furthermore, a concise representation of topfrequent-regular itemsets called top-frequent-regular closed itemsets [32] has recently been proposed to avoid redundancy of discovered top-frequentregular itemsets.…”

Section: Frequent-regular Itemset Mining (Frim)mentioning

confidence: 99%

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Mining High Utility Itemsets with Regular Occurrence

et al. 2016

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Abstract. High utility itemset mining (HUIM) plays an important role in the data mining community and in a wide range of applications. For example, in retail business it is used for finding sets of sold products that give high profit, low cost, etc. These itemsets can help improve marketing strategies, make promotions/ advertisements, etc. However, since HUIM only considers utility values of items/itemsets, it may not be sufficient to observe product-buying behavior of customers such as information related to "regular purchases of sets of products having a high profit margin". To address this issue, the occurrence behavior of itemsets (in the term of regularity) simultaneously with their utility values was investigated. Then, the problem of mining high utility itemsets with regular occurrence (MHUIR) to find sets of co-occurrence items with high utility values and regular occurrence in a database was considered. An efficient single-pass algorithm, called MHUIRA, was introduced. A new modified utility-list structure, called NUL, was designed to efficiently maintain utility values and occurrence information and to increase the efficiency of computing the utility of itemsets. Experimental studies on real and synthetic datasets and complexity analyses are provided to show the efficiency of MHUIRA combined with NUL in terms of time and space usage for mining interesting itemsets based on regularity and utility constraints.

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Section: Methodsmentioning

confidence: 75%

Section: Frequent-regular Itemset Mining (Frim)mentioning

confidence: 99%

Mining High Utility Itemsets with Regular Occurrence

et al. 2016

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“…This approach requires two database scans and uses the maximum occurrence interval of a pattern in a database to measure a pattern’s periodicity. Thus, many researchers are extending Tanbeer’s work to mine top−k [ 45 , 46 , 47 ] periodic patterns, but their approaches remain limited to k items. The work presented in [ 24 , 25 ] proposed an efficient and scalable regular mining algorithm with one database scan.…”

Section: Related Workmentioning

confidence: 99%

Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care

Ismail

Hassan

2017

Sensors

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The understanding of various health-oriented vital sign data generated from body sensor networks (BSNs) and discovery of the associations between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where occupants’ health status is continuously monitored remotely, it is essential to provide the required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach for mining the periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce productive-associated periodic-frequent patterns as the set of correlated periodic-frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients to raise the quality of diagnosis as well as improve treatment and smart care, especially for elderly people in smart homes. We develop an efficient algorithm named PPFP-growth (Productive Periodic-Frequent Pattern-growth) to discover all productive-associated periodic frequent patterns using these measures. PPFP-growth is efficient and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-growth algorithm, which can filter a huge number of periodic patterns to reveal only the correlated ones.

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“…In contrast, in [33] an efficient data structure is used to store and generalize low-level itemsets and association rules. Furthermore, the discovery of a succinct and non-redundant subset of frequent itemsets [5,10,20,32] has also been investigated. Since the above approaches do not address misleading generalized itemset mining, their goal is somehow related to but different from those addressed by this work.…”

Section: Related Workmentioning

confidence: 99%

Misleading Generalized Itemset discovery

Cagliero

Cerquitelli

Garza

et al. 2014

Expert Systems with Applications

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Frequent generalized itemset mining is a data mining technique utilized to discover a high-level view of interesting knowledge hidden in the analyzed data. By exploiting a taxonomy, patterns are usually extracted at any level of abstraction. However, some misleading high-level patterns could be included in the mined set. This paper proposes a novel generalized itemset type, namely the Misleading Generalized Itemset (MGI). Each MGI, denoted as X ⊲ E, represents a frequent generalized itemset X and its set E of low-level frequent descendants for which the correlation type is in contrast to the one of X. To allow experts to analyze the misleading high-level data correlations separately and exploit such knowledge by making different decisions, MGIs are extracted only if the low-level descendant itemsets that represent contrasting correlations cover almost the same portion of data as the high-level (misleading)ancestor. An algorithm to mine MGIs at the top of traditional generalized itemsets is also proposed.The experiments performed on both real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed approach.

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Mining top-k regular-frequent itemsets using database partitioning and support estimation

Cited by 16 publications

References 12 publications

Mining High Utility Itemsets with Regular Occurrence

Mining High Utility Itemsets with Regular Occurrence

Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care

Misleading Generalized Itemset discovery

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