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MEIT: Memory Efficient Itemset Tree for Targeted Association Rule Mining
"... Abstract. The Itemset Tree is an efficient data structure for performing targeted queries for itemset mining and association rule mining. It is incrementally up-datable by inserting new transactions and it provides efficient querying and up-dating algorithms. However, an important limitation of the ..."
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Abstract. The Itemset Tree is an efficient data structure for performing targeted queries for itemset mining and association rule mining. It is incrementally up-datable by inserting new transactions and it provides efficient querying and up-dating algorithms. However, an important limitation of the IT structure, con-cerning scalability, is that it consumes a large amount of memory. In this paper, we address this limitation by proposing an improved data structure named MEIT (Memory Efficient Itemset Tree). It offers an efficient node compression mechanism for reducing IT node size. It also performs on-the-fly node decom-pression for restoring compressed information when needed. An experimental study with datasets commonly used in the data mining literature representing various types of data shows that MEIT are up to 60 % smaller than IT (43 % on average).
A Recent Review on Itemset Tree Mining: MEIT Technique
"... Association rule mining forms the core of data mining and it is termed as one of the well-researched techniques of data mining. It aims to extract interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositorie ..."
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Association rule mining forms the core of data mining and it is termed as one of the well-researched techniques of data mining. It aims to extract interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories. Hence, Association rule mining is imperative to mine patterns and then generate rules from these obtained patterns. This paper provides the preliminaries of basic concepts about Itemset mining and survey the list of existing tree structure algorithms. These algorithms include various tasks such as fast query processing, optimizing memory space and reducing tree construction time. For mining maximal frequent pattern various algorithms used which optimization the search space for pruning.
Association Rules with Graph Patterns
"... We propose graph-pattern association rules (GPARs) for so-cial media marketing. Extending association rules for item-sets, GPARs help us discover regularities between entities in social graphs, and identify potential customers by exploring social influence. We study the problem of discovering top-k ..."
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We propose graph-pattern association rules (GPARs) for so-cial media marketing. Extending association rules for item-sets, GPARs help us discover regularities between entities in social graphs, and identify potential customers by exploring social influence. We study the problem of discovering top-k diversified GPARs. While this problem is NP-hard, we develop a parallel algorithm with accuracy bound. We also study the problem of identifying potential customers with GPARs. While it is also NP-hard, we provide a parallel scal-able algorithm that guarantees a polynomial speedup over sequential algorithms with the increase of processors. Using real-life and synthetic graphs, we experimentally verify the scalability and effectiveness of the algorithms. 1.
Mining Data Using Various Association Rule Mining Algorithms in Distributed Environment Using MPI
"... Abstract- Data mining combines machine learning, statistics and visualization techniques to discover and extract knowledge. In order to improve the efficiency of mining algorithm for the large data sets we are implementing Distributed Data Mining (DDM). In distributed association rule mining algorit ..."
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Abstract- Data mining combines machine learning, statistics and visualization techniques to discover and extract knowledge. In order to improve the efficiency of mining algorithm for the large data sets we are implementing Distributed Data Mining (DDM). In distributed association rule mining algorithm, one of the major challenges is to reduce the communication overhead. Data sites are required to exchange lot of information in the data mining process which may generates massive communication overhead. Message passing interface (MPI) is a technique to exchange information among a number of communicating nodes. Here we apply association rule mining algorithms like TopKRules and TNR algorithm in distributed environment using MPI for mining data within less communication overhead.
