Proceedings ArticleDOI
Application of Association Rule Mining: A case study on team India
K. A. A. D. Raj,Panchapakesan Padma +1 more
- pp 1-6
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TLDR
The outcome of the analysis reveals that Team India has performed well in the last ten years as compared to entire period since the team started playing its first match.Abstract:
This paper applies Association Rule Mining algorithm to sports management, especially mining relationship from data on performance of Indian cricket team in one day international (ODI) matches This analysis will help in determining factors associated with the match outcome so as to enable the team to formulate match winning strategies Data has been obtained from secondary sources to obtain deeper insights on playing conditions and the match outcome The association among factors such as outcome of toss, playing in a home ground or playing abroad, batting first or batting second, and the match outcome, ie, win or loss is examined The outcome of the analysis reveals that Team India has performed well in the last ten years (since 2001 to 2010) as compared to entire period since the team started playing its first match (since 1974 to 2010)read more
Citations
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References
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Proceedings ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
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Fast algorithms for mining association rules
TL;DR: Two new algorithms for solving thii problem that are fundamentally different from the known algorithms are presented and empirical evaluation shows that these algorithms outperform theknown algorithms by factors ranging from three for small problems to more than an order of magnitude for large problems.
Journal ArticleDOI
Mining frequent patterns without candidate generation
Jiawei Han,Jian Pei,Yiwen Yin +2 more
TL;DR: This study proposes a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develops an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth.
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An effective hash-based algorithm for mining association rules
TL;DR: The number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck, and allows us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly.
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Sampling Large Databases for Association Rules
TL;DR: New algorithms that reduce the database activity considerably by picking a Random sample, to find using this sample all association rules that probably hold in the whole database, and then to verify the results with the rest of the database.