Mining interesting itemsets from transactional database
01 Dec 2014-pp 1-4
TL;DR: Mining Interesting Itemsets (MIIS) algorithm is proposed which combines the features of partition algorithm and FP tree which reduces the database scan and produces the reduced itemsets from the transactions.
Abstract: Association rule mining is a standard technique used for finding the relationships among the itemsets in a database. The method of extracting the frequent itemsets from the database using existing algorithms has several disadvantages such as generation of large number of candidate itemsets, increase in computational time and database scan. With this aim, the paper proposes Mining Interesting Itemsets (MIIS) algorithm which combines the features of partition algorithm and FP tree which reduces the database scan and produces the reduced itemsets from the transactions. The reduced itemsets are validated using the mathematical measures.
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01 Oct 2017
TL;DR: The result of this study is useful for Indian team selector and manager of team, for strategic planning and decision making and increase winning for team India for the icc world cup 2017.
Abstract: Sport grown from their levels of country to international, essential part of any sport is measure performance analysis of players based on past statistics. Selection of team like cricket, based on player performance as strength and weakness is useful for team selector to make decision. Arrival of world cup, every team finds their best team combination to present on ground to get maximum result in their favour. Large dataset contain much useful information to identify that information uses association rule mining technique. This paper contains association rule mining technique based on individual specific Indian players and make set of players for cricket team. Player's statistics as past records, unknown relation of factors impact player performance, analyzing is helpful to identify best team as per terms. The factors as grounds, venue, strike rate, average, match played by particular player, runs scored, ball faced, high score, not out, match played, numbers of fours an sixes, number of hundreds and fifty's, position of batsman, dismissal, for bowlers factors as wicket taken, maiden's, over bowled, runs given(per match), economy rate, inning played. The result of this study is useful for Indian team selector and manager of team, for strategic planning and decision making and increase winning for team India for the icc world cup 2017.
5 citations
Cites methods from "Mining interesting itemsets from tr..."
...Hash apriori algorithm applies in steps, using partition algorithm it reduces record occupation time [4][8], dataset is scan only once after frequent itemset would inflect 1 to numbers of k itemset, after that using hash value in hash function it generate hash key that store in hash table for further use....
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TL;DR: The traditional Apriori algorithm is evaluated in terms of support and confidence of transactional itemsets and results of the traditional A Priori algorithm are presented.
Abstract: In Data mining the concept of association rule mining (ARM) is used to identify the frequent itemsets from large datasets. It defines frequent pattern mining from larger datasets using Apriori algorithm & FP-growth algorithm. The Apriori algorithm is a classic traditional algorithm for the mining all frequent itemsets and association rules. But, the traditional Apriori algorithm have some limitations i.e. there are more candidate sets generation & huge memory consumption, etc. Still, there is a scope for improvement to modify the existing Apriori algorithm for identifying frequent itemsets with a focus on reducing the computational time and memory space. This paper presents the analysis of existing Apriori algorithms and results of the traditional Apriori algorithm. Experimentation carried out on transactional database i.e. retail market for getting frequent itemsets. The traditional Apriori algorithm is evaluated in terms of support and confidence of transactional itemsets. Keyword Frequent itemsets; Association Rule Mining; Frequent pattern mining; Apriori; FP-growth
2 citations
Cites methods from "Mining interesting itemsets from tr..."
...[18] proposed the interesting itemsets algorithm which first performs the preprocessing on database and then remove redundancy among the data....
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References
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Book•
08 Sep 2000TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data
23,600 citations
"Mining interesting itemsets from tr..." refers background in this paper
...Let us consider a dataset in Table II from [7] with min_sup=2 where TID refers to Transaction Identifier....
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01 Jun 1993
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.
Abstract: We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel estimation and pruning techniques. We also present results of applying this algorithm to sales data obtained from a large retailing company, which shows the effectiveness of the algorithm.
15,645 citations
16 May 2000
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.
Abstract: Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns.In this study, we propose a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a highly condensed, much smaller data structure, which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent pattern mining methods.
6,118 citations
TL;DR: An efficient algorithm is presented that generates all significant transactions in a large database of customer transactions that consists of items purchased by a customer in a visit.
Abstract: We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant assoc...
3,198 citations
01 Jan 2006
TL;DR: The preliminaries of basic concepts about association rule mining are provided and the list of existing association rulemining techniques are surveyed.
Abstract: In this paper, we provide the preliminaries of basic concepts about association rule mining and survey the list of existing association rule mining techniques. Of course, a single article cannot be a complete review of all the al- gorithms, yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions that have yet to be explored.
485 citations
"Mining interesting itemsets from tr..." refers methods in this paper
...The transactions from the database are selected as the input for MIIS algorithm in order to obtain the frequent itemsets....
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