Mining coverage patterns from transactional databases

Srinivas, Preethi; Reddy, P. Krishna; Trinath, A. V.; Bhargav, Shreya; Kiran, R. Uday

doi:10.1007/s10844-014-0318-3

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…However, these works explore the notion of coverage for a single graph as opposed to a GTD, which is our focus. Moreover, the works in [ 18 , 36 ] find coverage patterns in transactional data using pattern-growth and level-wise pruning approaches, respectively.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…We shall now explain the concept of coverage patterns [ 18 , 36 ]. Coverage patterns are characterized by the notions of relative frequency, coverage support and overlap ratio.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…Given the values of minRF , minCS and maxOR , a pattern P ={ , , , } is considered as a coverage pattern if RF ( ) minRF , CS(P) minCS and OR(P) maxOR . By exploiting the sorted closure property of overlap ratio , a level-wise apriori-based approach has been proposed in [ 36 ] and a pattern-growth-based approach has been proposed in [ 18 ] for extracting all coverage patterns from D , given minRF , minCS and maxOR values. Additionally, a MapReduce-based algorithm to extract coverage patterns has been proposed in [ 32 ].…”

Section: Related Work and Backgroundmentioning

confidence: 99%

“…Knowledge concerning the coverage aspect of a set of subgraphs can be valuable for improving the performance of several applications. In the literature, the notion of coverage has been well explored in set theory and graph theory [ 7 , 11 – 13 , 15 , 19 , 27 , 39 , 47 ] as well as the extraction of coverage patterns from transactional data [ 18 , 36 ]. However, none of the existing works have investigated the issue of extracting the coverage-related knowledge of patterns from GTD.…”

Section: Introductionmentioning

confidence: 99%

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Mining subgraph coverage patterns from graph transactions

Reddy

Mondal

2021

Int J Data Sci Anal

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Pattern mining from graph transactional data (GTD) is an active area of research with applications in the domains of bioinformatics, chemical informatics and social networks. Existing works address the problem of mining frequent subgraphs from GTD. However, the knowledge concerning the coverage aspect of a set of subgraphs is also valuable for improving the performance of several applications. In this regard, we introduce the notion of subgraph coverage patterns ( SCPs ). Given a GTD, a subgraph coverage pattern is a set of subgraphs subject to relative frequency, coverage and overlap constraints provided by the user. We propose the S ubgraph I D-based F lat T ransactional ( SIFT ) framework for the efficient extraction of SCPs from a given GTD. Our performance evaluation using three real datasets demonstrates that our proposed SIFT framework is indeed capable of efficiently extracting SCPs from GTD. Furthermore, we demonstrate the effectiveness of SIFT through a case study in computer-aided drug design.

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Section: Related Work and Backgroundmentioning

confidence: 99%

“…We shall now explain the concept of coverage patterns [ 18 , 36 ]. Coverage patterns are characterized by the notions of relative frequency, coverage support and overlap ratio.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Section: Related Work and Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mining subgraph coverage patterns from graph transactions

Reddy

Mondal

2021

Int J Data Sci Anal

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“…Since Agrawal et al proposed association rule mining, both data mining and recommendation systems have been developing rapidly. On the one hand, sequential patterns [2], sequential rules [3], coverage patterns [4], temporal patterns [5], subgraph patterns [6] and periodic patterns [7] have been proposed. Data mining, as an increasingly sophisticated technology, has been used for many domains, such as time series analysis [8], medicine [9] and image processing [10].…”

Section: Introductionmentioning

confidence: 99%

A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

Chen

2021

Entropy

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Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.

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An Efficient Distributed Coverage Pattern Mining Algorithm

Sathineni

Reddy

et al. 2021

Big Data Analytics

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Mining coverage patterns from transactional databases

Cited by 13 publications

References 13 publications

Mining subgraph coverage patterns from graph transactions

Mining subgraph coverage patterns from graph transactions

A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

An Efficient Distributed Coverage Pattern Mining Algorithm

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