Efficient High-Utility Itemset Mining Over Variety of Databases: A Survey

Suvarna, U.; Srinivas, Y.

doi:10.1007/978-981-13-0514-6_76

Cited by 4 publications

(2 citation statements)

References 54 publications

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“…HUPM finds application both in classical contexts where (normal) itemset mining was born, such as market analysis, and in novel ones such as biomedicine, mobile computing, etc. (Suvarna and Srinivas 2019). Thanks to its exhibited versatility, a huge volume of research studies has been produced regarding HUPM and its variants.…”

Section: Related Workmentioning

confidence: 99%

Extended High Utility Pattern Mining: An Answer Set Programming Based Framework and Applications

Cauteruccio¹,

Terracina²

2023

Preprint

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Detecting sets of relevant patterns from a given dataset is an important challenge in data mining. The relevance of a pattern, also called utility in the literature, is a subjective measure and can be actually assessed from very different points of view. Rule-based languages like Answer Set Programming (ASP) seem well suited for specifying user-provided criteria to assess pattern utility in a form of constraints; moreover, declarativity of ASP allows for a very easy switch between several criteria in order to analyze the dataset from different points of view. In this paper, we make steps toward extending the notion of High Utility Pattern Mining (HUPM); in particular we introduce a new framework that allows for new classes of utility criteria not considered in the previous literature. We also show how recent extensions of ASP with external functions can support a fast and effective encoding and testing of the new framework. To demonstrate the potential of the proposed framework, we exploit it as a building block for the definition of an innovative method for predicting ICU admission for COVID-19 patients. Finally, an extensive experimental activity demonstrates both from a quantitative and a qualitative point of view the effectiveness of the proposed approach. Under consideration in Theory and Practice of Logic Programming (TPLP).

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Section: Related Workmentioning

confidence: 99%

Extended High Utility Pattern Mining: An Answer Set Programming Based Framework and Applications

Cauteruccio¹,

Terracina²

2023

Preprint

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“…In this section, we briefly discuss various approaches in the context of periodicity mining, which is subfield of pattern mining. Pattern mining has other subfields depending on the type of mined patterns such as frequent item sets [3], sequential patterns [8,11,17,18], high utility patterns [19][20][21][22], periodic patterns [23][24][25], as well as flexible periodic patterns [9,12,26]. Chanda et al [12] put forth an algorithm recently that finds flexible periodic patterns in a time series by using suffix tree data structure.…”

Section: Literature Reviewmentioning

confidence: 99%

HOVA-FPPM: Flexible Periodic Pattern Mining in Time Series Databases Using Hashed Occurrence Vectors and Apriori Approach

Javed

Nawaz

Khan

2021

Scientific Programming

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Finding flexible periodic patterns in a time series database is nontrivial due to irregular occurrence of unimportant events, which makes it intractable or computationally intensive for large datasets. There exist various solutions based on Apriori, projection, tree, and other techniques to mine these patterns. However, the existence of constant size tree structure, i.e., suffix tree, with extra information in memory throughout the mining process, redundant and invalid pattern generation, limited types of mined flexible periodic patterns, and repeated traversal over tree data structure for pattern discovery, results in unacceptable space and time complexity. In order to overcome these issues, we introduce an efficient approach called HOVA-FPPM based on Apriori approach with hashed occurrence vectors to find all types of flexible periodic patterns. We do not rely on complex tree structure rather manage necessary information in a hash table for efficient lookup during the mining process. We measured the performance of our proposed approach and compared the results with the baseline approach, i.e., FPPM. The results show that our approach requires lesser time and space, regardless of the data size or period value.

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High utility itemsets mining from transactional databases: a survey

Kumar,

Singh

2023

Appl Intell

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Efficient High-Utility Itemset Mining Over Variety of Databases: A Survey

Cited by 4 publications

References 54 publications

Extended High Utility Pattern Mining: An Answer Set Programming Based Framework and Applications

Extended High Utility Pattern Mining: An Answer Set Programming Based Framework and Applications

HOVA-FPPM: Flexible Periodic Pattern Mining in Time Series Databases Using Hashed Occurrence Vectors and Apriori Approach

High utility itemsets mining from transactional databases: a survey

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