Turbo-charging vertical mining of large databases

Shenoy, Pradeep; Haritsa, Jayant R.; Sundarshan, S.; Bhalotia, Gaurav; Bawa, Mayank; Shah, Devavrat

doi:10.1145/342009.335376

Cited by 134 publications

(78 citation statements)

References 2 publications

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“…Some of the algorithms which work on hybrid approach are AprioriHybrid [18], Viper [19] , Pincer Search [20]and MaxClique [21]. Fig.3 below shows an example of the two approaches [20].…”

Section: Hybrid Approachmentioning

confidence: 99%

“…If Ck remains large until nearly the end and then has an abrupt drop, there gain will be no gain by using AprioriHybrid since we can use AprioriTid only for a short period of time after the switch. Another algorithm VIPER [19] is also based on hybrid approach. It is general-purpose, making no special requirements of the underlying database.…”

Section: Hybrid Approachmentioning

confidence: 99%

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Efficiently Mining Frequent Itemsets using Various Approaches: A Survey

Dhote¹,

Rathi²

2012

IJCA

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In this paper we present the various elementary traversal approaches for mining association rules. We start with a formal definition of association rule and its basic algorithm. We then discuss the association rule mining algorithms from several perspectives such as breadth first approach, depth first approach and Hybrid approach. Comparison of the various approaches is done in terms of time complexity and I/O overhead on CPU. Finally, this paper prospects the association rule mining and discuss the areas where there is scope for scalability.

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“…Some of the algorithms which work on hybrid approach are AprioriHybrid [18], Viper [19] , Pincer Search [20]and MaxClique [21]. Fig.3 below shows an example of the two approaches [20].…”

Section: Hybrid Approachmentioning

confidence: 99%

Section: Hybrid Approachmentioning

confidence: 99%

Efficiently Mining Frequent Itemsets using Various Approaches: A Survey

Dhote¹,

Rathi²

2012

IJCA

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“…This layout could be maintained as a bit vector. It has been shown that vertical layout performs generally better than the horizontal format [10,11]. Table 1, Table 2 and Table 3 show examples for different types of layouts.…”

Section: Data Representationmentioning

confidence: 99%

Software Specifications Mining using Transaction Mapping Algorithm

Jeevarathinam¹,

Thanamani²

2010

IJCA

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Specification mining is a dynamic analysis process aimed at automatically inferring suggested specifications of a program from its execution traces. In software development it would be preferable if all programs and software projects are developed with clear, precise and documented specifications. But due to hard deadlines and `short-time-to-market' requirement, software products often come with project oriented, incomplete and even without any documented specifications. This situation is further motivated by a phenomenon termed as software evolution. As software evolves the documented specification is often not updated. This might render the original documented specification of little use after several cycles of program evolution. The above factors have contributed to high software maintenance costs. In this paper a novel technique to efficiently mine software specifications, called TM_TraceMiner is proposed which mines software specifications from program execution traces. To address the limitations of Apriori-like methods and FP-growth methods, a mining paradigm has been proposed, which uses Transaction Mapping algorithm.

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“…An alternative way is to represent a database in vertical format, i.e., each item is associated with a set of transaction identifiers (TIDs) that include the item. As a representative in this group, VIPER [17] uses a vertical bit-vector with compression to store intermediate data during execution and performs counting with a vertical TID-list approach. FP-growth [11] is a fundamentally different algorithm from the Apriori-like algorithms.…”

Section: Related Workmentioning

confidence: 99%

Discovery of maximum length frequent itemsets

Sung

Xiong

et al. 2008

Information Sciences

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The use of frequent itemsets has been limited by the high computational cost as well as the large number of resulting itemsets. In many real-world scenarios, however, it is often sufficient to mine a small representative subset of frequent itemsets with low computational cost. To that end, in this paper, we define a new problem of finding the frequent itemsets with a maximum length and present a novel algorithm to solve this problem. Indeed, maximum length frequent itemsets can be efficiently identified in very large data sets and are useful in many application domains. Our algorithm generates the maximum length frequent itemsets by adapting a pattern fragment growth methodology based on the FP-tree structure. Also, a number of optimization techniques have been exploited to prune the search space. Finally, extensive experiments on real-world data sets validate the proposed algorithm.

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Turbo-charging vertical mining of large databases

Cited by 134 publications

References 2 publications

Efficiently Mining Frequent Itemsets using Various Approaches: A Survey

Efficiently Mining Frequent Itemsets using Various Approaches: A Survey

Software Specifications Mining using Transaction Mapping Algorithm

Discovery of maximum length frequent itemsets

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