Mining Top-K Periodic-Frequent Pattern from Transactional Databases without Support Threshold

Amphawan, Komate; Lenca, Philippe; Surarerks, Athasit

doi:10.1007/978-3-642-10392-6_3

Cited by 61 publications

(26 citation statements)

References 26 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: Methodssupporting

confidence: 84%

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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: Methodssupporting

confidence: 84%

“…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].…”

Section: Frequent-regular Itemset Mining (Frim)mentioning

confidence: 99%

Mining High Utility Itemsets with Regular Occurrence

et al. 2016

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“…However, these algorithms are not designed to discover Proceedings of the 2nd Czech-China Scientific Conference 2016periodic patterns. Inspired by the work on FIM, researchers have designed several algorithms to discover periodic frequent patterns (PFP) in transaction databases [4][5][6][7][8][9]. Several applications of mining periodic frequent patterns have been reported in previous work [9].…”

Section: Related Workmentioning

confidence: 99%

“…Several algorithms have been proposed to discover periodic frequent patterns (PFP) [4][5][6][7][8][9] in a transaction database (a sequence of transactions). Typically, periodic pattern mining algorithms will discard a pattern as being nonperiodic if it has a single period greater than a maximal periodicity threshold, defined by the user.…”

Section: Introductionmentioning

confidence: 99%

PFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures

Fournier‐Viger¹,

Lin²,

Duong³

et al. 2017

Proceedings of the 2nd Czech-China Scientific Conference 2016

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Periodic pattern mining is the task of discovering patterns that periodically appear in transactions. Typically, periodic pattern mining algorithms will discard a pattern as being nonperiodic if it has a single period greater than a maximal periodicity threshold, defined by the user. A major drawback of this approach is that it is not flexible, as a pattern can be discarded based on only one of its periods. In this chapter, we present a solution to this issue by proposing to discover periodic patterns using three measures: the minimum periodicity, the maximum periodicity, and the average periodicity. The combination of these measures has the advantage of being more flexible. Properties of these measures are studied. Moreover, an efficient algorithm named PFPM (Periodic Frequent Pattern Miner) is proposed to discover all frequent periodic patterns using these measures. An experimental evaluation on real data sets shows that the proposed PFPM algorithm is efficient and can filter a huge number of nonperiodic patterns to reveal only the desired periodic patterns.

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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 [42,43,44] 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 Pattern in Body Sensor Data for Smart Home Care

Ismail¹,

Hassan²

2017

Preprint

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Abstract: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 to mine the periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce productiveassociated 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 a smart home. 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.Keywords: Body sensor network; Smart home, knowledge discovery in BSN data; frequent patterns; periodic patterns and productive pattern.

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Mining Top-K Periodic-Frequent Pattern from Transactional Databases without Support Threshold

Cited by 61 publications

References 26 publications

Mining High Utility Itemsets with Regular Occurrence

Mining High Utility Itemsets with Regular Occurrence

PFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures

Mining Productive-associated Periodic Frequent Pattern in Body Sensor Data for Smart Home Care

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