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Krishna P Reddy - One of the best experts on this subject based on the ideXlab platform.

  • discovering partial periodic frequent patterns in a Transactional Database
    Journal of Systems and Software, 2017
    Co-Authors: Uday R Kiran, J N Venkatesh, Masashi Toyoda, Masaru Kitsuregawa, Krishna P Reddy
    Abstract:

    Proposed a novel model to find partial periodic-frequent patterns in a Database.Introduced a measure to find partial periodic-frequent patterns in a Database.An efficient pruning technique has been proposed to reduce the computational cost.Described a pattern-growth algorithm to find all partial periodic-frequent patternsExperimental results show that our model is efficient. Time and frequency are two important dimensions to determine the interestingness of a pattern in a Database. Periodic-frequent patterns are an important class of regularities that exist in a Database with respect to these two dimensions. Current studies on periodic-frequent pattern mining have focused on discovering full periodic-frequent patterns, i.e., finding all frequent patterns that have exhibited complete cyclic repetitions in a Database. However, partial periodic-frequent patterns are more common due to the imperfect nature of real-world. This paper proposes a flexible and generic model to find partial periodic-frequent patterns. A new interesting measure, periodic-ratio, has been introduced to determine the periodic interestingness of a frequent pattern by taking into account its proportion of cyclic repetitions in a Database. The proposed patterns do not satisfy the anti-monotonic property. A novel pruning technique has been introduced to reduce the search space effectively. A pattern-growth algorithm to find all partial periodic-frequent patterns has also been presented in this paper. Experimental results demonstrate that the proposed model can discover useful information, and the algorithm is efficient.

  • extracting diverse patterns with unbalanced concept hierarchy
    Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2014
    Co-Authors: Kumara M Swamy, Krishna P Reddy, Somya Srivastava
    Abstract:

    The process of frequent pattern extraction finds interesting information about the association among the items in a Transactional Database. The notion of support is employed to extract the frequent patterns. Normally, in a given domain, a set of items can be grouped into a category and a pattern may contain the items which belong to multiple categories. In several applications, it may be useful to distinguish between the pattern having items belonging to multiple categories and the pattern having items belonging to one or a few categories. The notion of diversity captures the extent the items in the pattern belong to multiple categories. The items and the categories form a concept hierarchy. In the literature, an approach has been proposed to rank the patterns by considering the balanced concept hierarchy. In a real life scenario, the concept hierarchies are normally unbalanced. In this paper, we propose a general approach to calculate the rank based on the diversity, called drank, by considering the unbalanced concept hierarchy. The experiment results show that the patterns ordered based on drank are different from the patterns ordered based on support, and the proposed approach could assign the drank to different kinds of unbalanced patterns.

Masaru Kitsuregawa - One of the best experts on this subject based on the ideXlab platform.

  • DaWaK - An Efficient Map-Reduce Framework to Mine Periodic Frequent Patterns
    Big Data Analytics and Knowledge Discovery, 2017
    Co-Authors: Alampally Anirudh, P. Krishna Reddy, Masashi Toyoda, R. Uday Kiran, Masaru Kitsuregawa
    Abstract:

    Periodic Frequent patterns (PFPs) are an important class of regularities that exist in a Transactional Database. In the literature, pattern growth-based approaches to mine PFPs have be proposed by considering a single machine. In this paper, we propose a Map-Reduce framework to mine PFPs by considering multiple machines. We have proposed a parallel algorithm by including the step of distributing Transactional identifiers among the machines. Further, the notion of partition summary has been proposed to reduce the amount of data shuffled among the machines. Experiments on Apache Spark’s distributed environment show that the proposed approach speeds up with the increase in number of machines and the notion of partition summary significantly reduces the amount of data shuffled among the machines.

  • discovering partial periodic frequent patterns in a Transactional Database
    Journal of Systems and Software, 2017
    Co-Authors: Uday R Kiran, J N Venkatesh, Masashi Toyoda, Masaru Kitsuregawa, Krishna P Reddy
    Abstract:

    Proposed a novel model to find partial periodic-frequent patterns in a Database.Introduced a measure to find partial periodic-frequent patterns in a Database.An efficient pruning technique has been proposed to reduce the computational cost.Described a pattern-growth algorithm to find all partial periodic-frequent patternsExperimental results show that our model is efficient. Time and frequency are two important dimensions to determine the interestingness of a pattern in a Database. Periodic-frequent patterns are an important class of regularities that exist in a Database with respect to these two dimensions. Current studies on periodic-frequent pattern mining have focused on discovering full periodic-frequent patterns, i.e., finding all frequent patterns that have exhibited complete cyclic repetitions in a Database. However, partial periodic-frequent patterns are more common due to the imperfect nature of real-world. This paper proposes a flexible and generic model to find partial periodic-frequent patterns. A new interesting measure, periodic-ratio, has been introduced to determine the periodic interestingness of a frequent pattern by taking into account its proportion of cyclic repetitions in a Database. The proposed patterns do not satisfy the anti-monotonic property. A novel pruning technique has been introduced to reduce the search space effectively. A pattern-growth algorithm to find all partial periodic-frequent patterns has also been presented in this paper. Experimental results demonstrate that the proposed model can discover useful information, and the algorithm is efficient.

  • DNIS - Discovering Chronic-Frequent Patterns in Transactional Databases
    Databases in Networked Information Systems, 2015
    Co-Authors: R. Uday Kiran, Masaru Kitsuregawa
    Abstract:

    This paper investigates the partial periodic behavior of the frequent patterns in a Transactional Database, and introduces a new class of user-interest-based patterns known as chronic-frequent patterns. Informally, a frequent pattern is said to be chronic if it has sufficient number of cyclic repetitions in a Database. The proposed patterns can provide useful information to the users in many real-life applications. An example is finding chronic diseases in a medical Database. The chronic-frequent patterns satisfy the anti-monotonic property. This property makes the pattern mining practicable in real-world applications. The existing pattern growth techniques that are meant to discover frequent patterns cannot be used for finding the chronic-frequent patterns. The reason is that the tree structure employed by these techniques’ capture only the frequency and disregards the periodic behavior of the patterns. We introduce another pattern-growth algorithm which employs an alternative tree structure, called Chronic-Frequent pattern tree (CFP-tree), to capture both frequency and periodic behavior of the patterns. Experimental results show that the proposed patterns can provide useful information and our algorithm is efficient.

  • SSDBM - Towards efficient discovery of coverage patterns in Transactional Databases
    Proceedings of the 25th International Conference on Scientific and Statistical Database Management - SSDBM, 2013
    Co-Authors: R. Uday Kiran, Masashi Toyoda, Masaru Kitsuregawa
    Abstract:

    Coverage pattern mining is an important model in data mining. It provides useful information pertaining to the sets of items that have coverage interesting to the users in a Transactional Database. The coverage patterns do not satisfy the anti-monotonic property. This increases the search space in the itemset lattice, which in turn increases the computational cost of mining these patterns. An Apriori-like algorithm known as CMine has been proposed in the literature to discover the patterns. It employs a pruning technique to reduce the search space. We have observed that there exists further scope for reducing the search space effectively. In this paper, we theoretically analyze different measures used in the pattern model, and introduce a novel pruning technique to reduce the search space. An Apriori-like algorithm, called CMine++, has also been proposed to discover the patterns. The performance study shows that mining coverage patterns with CMine++ is efficient.

Youngkoo Lee - One of the best experts on this subject based on the ideXlab platform.

  • rp tree a tree structure to discover regular patterns in Transactional Database
    Intelligent Data Engineering and Automated Learning, 2008
    Co-Authors: Syed Khairuzzaman Tanbeer, Chowdhury Farhan Ahmed, Byeongsoo Jeong, Youngkoo Lee
    Abstract:

    Temporal regularity of pattern appearance can be regarded as an important criterion for measuring the interestingness in several applications like market basket analysis, web administration, gene data analysis, network monitoring, and stock market. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing works is appropriate for discovering the patterns that occur regularly in a Transactional Database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from Transactional Databases and propose an efficient data structure, called Regular Pattern tree (RP-tree in short), that enables a pattern growth-based mining technique to generate the complete set of regular patterns in a Database for a user-given regularity threshold. Our comprehensive experimental study shows that RP-tree is both time and memory efficient in finding regular pattern.

  • Mining Regular Patterns in Transactional Databases
    IEICE Transactions on Information and Systems, 2008
    Co-Authors: Syed Khairuzzaman Tanbeer, Chowdhury Farhan Ahmed, Byeongsoo Jeong, Youngkoo Lee
    Abstract:

    The frequency of a pattern may not be a sufficient criterion for identifying meaningful patterns in a Database. The temporal regularity of a pattern can be another key criterion for assessing the importance of a pattern in several applications. A pattern can be said regular if it appears at a regular user-defined interval in the Database. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing studies have provided an appropriate method for discovering the patterns that occur regularly in a Transactional Database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from Transactional Databases. We also devise an efficient tree-based data structure, called a Regular Pattern tree (RP-tree in short), that captures the Database contents in a highly compact manner and enables a pattern growth-based mining technique to generate the complete set of regular patterns in a Database for a user-defined regularity threshold. Our performance study shows that mining regular patterns with an RP-tree is time and memory efficient, as well as highly scalable.

  • IDEAL - RP-Tree: A Tree Structure to Discover Regular Patterns in Transactional Database
    Lecture Notes in Computer Science, 2008
    Co-Authors: Syed Khairuzzaman Tanbeer, Chowdhury Farhan Ahmed, Byeongsoo Jeong, Youngkoo Lee
    Abstract:

    Temporal regularity of pattern appearance can be regarded as an important criterion for measuring the interestingness in several applications like market basket analysis, web administration, gene data analysis, network monitoring, and stock market. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing works is appropriate for discovering the patterns that occur regularly in a Transactional Database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from Transactional Databases and propose an efficient data structure, called Regular Pattern tree (RP-tree in short), that enables a pattern growth-based mining technique to generate the complete set of regular patterns in a Database for a user-given regularity threshold. Our comprehensive experimental study shows that RP-tree is both time and memory efficient in finding regular pattern.

Somya Srivastava - One of the best experts on this subject based on the ideXlab platform.

  • extracting diverse patterns with unbalanced concept hierarchy
    Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2014
    Co-Authors: Kumara M Swamy, Krishna P Reddy, Somya Srivastava
    Abstract:

    The process of frequent pattern extraction finds interesting information about the association among the items in a Transactional Database. The notion of support is employed to extract the frequent patterns. Normally, in a given domain, a set of items can be grouped into a category and a pattern may contain the items which belong to multiple categories. In several applications, it may be useful to distinguish between the pattern having items belonging to multiple categories and the pattern having items belonging to one or a few categories. The notion of diversity captures the extent the items in the pattern belong to multiple categories. The items and the categories form a concept hierarchy. In the literature, an approach has been proposed to rank the patterns by considering the balanced concept hierarchy. In a real life scenario, the concept hierarchies are normally unbalanced. In this paper, we propose a general approach to calculate the rank based on the diversity, called drank, by considering the unbalanced concept hierarchy. The experiment results show that the patterns ordered based on drank are different from the patterns ordered based on support, and the proposed approach could assign the drank to different kinds of unbalanced patterns.

  • PAKDD (1) - Extracting Diverse Patterns with Unbalanced Concept Hierarchy
    Advances in Knowledge Discovery and Data Mining, 2014
    Co-Authors: M Kumara Swamy, P. Krishna Reddy, Somya Srivastava
    Abstract:

    The process of frequent pattern extraction finds interesting information about the association among the items in a Transactional Database. The notion of support is employed to extract the frequent patterns. Normally, in a given domain, a set of items can be grouped into a category and a pattern may contain the items which belong to multiple categories. In several applications, it may be useful to distinguish between the pattern having items belonging to multiple categories and the pattern having items belonging to one or a few categories. The notion of diversity captures the extent the items in the pattern belong to multiple categories. The items and the categories form a concept hierarchy. In the literature, an approach has been proposed to rank the patterns by considering the balanced concept hierarchy. In a real life scenario, the concept hierarchies are normally unbalanced. In this paper, we propose a general approach to calculate the rank based on the diversity, called drank, by considering the unbalanced concept hierarchy. The experiment results show that the patterns ordered based on drank are different from the patterns ordered based on support, and the proposed approach could assign the drank to different kinds of unbalanced patterns.

  • COMAD - Discovering diverse-frequent patterns in Transactional Databases
    2011
    Co-Authors: Somya Srivastava, R. Uday Kiran, P. Krishna Reddy
    Abstract:

    In the area of data mining, the process of frequent pattern extraction finds interesting information about the association among the items in a Transactional Database. The notion of support is employed to extract the frequent patterns. Normally, a frequent pattern may contain items which belong to different categories of a particular domain. The existing approaches do not consider the notion of diversity while extracting the frequent patterns. For certain types of applications, it may be useful to distinguish between the frequent patterns with items belonging to different categories and the frequent patterns with items belonging to the same category. In this paper we propose a new interestingness measure, called DiverseRank, to rank the frequent patterns based on the items' categories. Given a set of frequent patterns, we propose an efficient algorithm to extract the diverse-frequent patterns. Experiments on the real-world data set show that the diverse-frequent patterns extracted with the proposed DiverseRank measure are different from the frequent patterns extracted with the support measure.

Byeongsoo Jeong - One of the best experts on this subject based on the ideXlab platform.

  • DASFAA (1) - Privacy preserving mining maximal frequent patterns in Transactional Databases
    Database Systems for Advanced Applications, 2012
    Co-Authors: Rezaul Karim, Byeongsoo Jeong, Mamunur Rashid, Ho-jin Choi
    Abstract:

    Problem of finding frequent patterns has long been studied because it is very essential to data mining tasks such as association rule analysis, clustering, and classification analysis. Privacy preserving data mining is another important issue for this domain since most users do not want their private information to leak out. In this paper, we proposed an efficient approach for mining maximal frequent patterns from a large Transactional Database with privacy preserving capability. As for privacy preserving, we utilized prime number based data transformation method. We also developed a noble algorithm for mining maximal frequent patterns based on lattice structure. Extensive performance analysis shows the effectiveness of our approach.

  • rp tree a tree structure to discover regular patterns in Transactional Database
    Intelligent Data Engineering and Automated Learning, 2008
    Co-Authors: Syed Khairuzzaman Tanbeer, Chowdhury Farhan Ahmed, Byeongsoo Jeong, Youngkoo Lee
    Abstract:

    Temporal regularity of pattern appearance can be regarded as an important criterion for measuring the interestingness in several applications like market basket analysis, web administration, gene data analysis, network monitoring, and stock market. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing works is appropriate for discovering the patterns that occur regularly in a Transactional Database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from Transactional Databases and propose an efficient data structure, called Regular Pattern tree (RP-tree in short), that enables a pattern growth-based mining technique to generate the complete set of regular patterns in a Database for a user-given regularity threshold. Our comprehensive experimental study shows that RP-tree is both time and memory efficient in finding regular pattern.

  • Mining Regular Patterns in Transactional Databases
    IEICE Transactions on Information and Systems, 2008
    Co-Authors: Syed Khairuzzaman Tanbeer, Chowdhury Farhan Ahmed, Byeongsoo Jeong, Youngkoo Lee
    Abstract:

    The frequency of a pattern may not be a sufficient criterion for identifying meaningful patterns in a Database. The temporal regularity of a pattern can be another key criterion for assessing the importance of a pattern in several applications. A pattern can be said regular if it appears at a regular user-defined interval in the Database. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing studies have provided an appropriate method for discovering the patterns that occur regularly in a Transactional Database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from Transactional Databases. We also devise an efficient tree-based data structure, called a Regular Pattern tree (RP-tree in short), that captures the Database contents in a highly compact manner and enables a pattern growth-based mining technique to generate the complete set of regular patterns in a Database for a user-defined regularity threshold. Our performance study shows that mining regular patterns with an RP-tree is time and memory efficient, as well as highly scalable.

  • IDEAL - RP-Tree: A Tree Structure to Discover Regular Patterns in Transactional Database
    Lecture Notes in Computer Science, 2008
    Co-Authors: Syed Khairuzzaman Tanbeer, Chowdhury Farhan Ahmed, Byeongsoo Jeong, Youngkoo Lee
    Abstract:

    Temporal regularity of pattern appearance can be regarded as an important criterion for measuring the interestingness in several applications like market basket analysis, web administration, gene data analysis, network monitoring, and stock market. Even though there have been some efforts to discover periodic patterns in time-series and sequential data, none of the existing works is appropriate for discovering the patterns that occur regularly in a Transactional Database. Therefore, in this paper, we introduce a novel concept of mining regular patterns from Transactional Databases and propose an efficient data structure, called Regular Pattern tree (RP-tree in short), that enables a pattern growth-based mining technique to generate the complete set of regular patterns in a Database for a user-given regularity threshold. Our comprehensive experimental study shows that RP-tree is both time and memory efficient in finding regular pattern.