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170
Exploratory Mining and Pruning Optimizations of Constrained Associations Rules
, 1998
"... From the standpoint of supporting humancentered discovery of knowledge, the presentday model of mining association rules suffers from the following serious shortcom ings: (i) lack of user exploration and control, (ii) lack of focus, and (iii) rigid notion of relationships. In effect, this model f ..."
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Cited by 275 (42 self)
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From the standpoint of supporting humancentered discovery of knowledge, the presentday model of mining association rules suffers from the following serious shortcom ings: (i) lack of user exploration and control, (ii) lack of focus, and (iii) rigid notion of relationships. In effect, this model functions as a blackbox, admitting little user interaction in between. We propose, in this paper, an architecture that opens up the blackbox, and supports constraintbased, humancentered exploratory mining of associations. The foundation of this architecture is a rich set of con straint constructs, including domain, class, and $QLstyle aggregate constraints, which enable users to clearly specify what associations are to be mined. We propose constrained association queries as a means of specifying the constraints to be satisfied by the antecedent and consequent of a mined association.
Mining Association Rules with Item Constraints
"... The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In practice, users are often interested in a subset of association rules. For example, they may only want rules that contain a speci ..."
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Cited by 249 (0 self)
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The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In practice, users are often interested in a subset of association rules. For example, they may only want rules that contain a specific item or rules that contain children of a specific item in a hierarchy. While such constraints can be applied as a postprocessing step, integrating them into the mining algorithm can dramatically reduce the execution time. We consider the problem of integrating constraints that are boolean expressions over the presence or absence of items into the association discovery algorithm. We present three integrated algorithms for mining association rules with item constraints and discuss their tradeoffs. 1. Introduction The problem of discovering association rules was introduced in (Agrawal, Imielinski, & Swami 1993). Given a set of transactions, where each transaction is a set of literals (call...
Scalable Algorithms for Association Mining
 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2000
"... Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets, and then forming conditional implication rules among them. In this paper we present efficient algorithms for the discovery ..."
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Cited by 193 (22 self)
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Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets, and then forming conditional implication rules among them. In this paper we present efficient algorithms for the discovery of frequent itemsets, which forms the compute intensive phase of the task. The algorithms utilize the structural properties of frequent itemsets to facilitate fast discovery. The items are organized into a subset lattice search space, which is decomposed into small independent chunks or sublattices, which can be solved in memory. Efficient lattice traversal techniques are presented, which quickly identify all the long frequent itemsets, and their subsets if required. We also present the effect of using different database layout schemes combined with the proposed decomposition and traversal techniques. We experimentally compare the new algorithms against the previous approaches, obtaining ...
A tree projection algorithm for generation of frequent itemsets
 Journal of Parallel and Distributed Computing
, 2000
"... In this paper we propose algorithms for generation of frequent itemsets by successive construction of the nodes of a lexicographic tree of itemsets. We discuss di erent strategies in generation and traversal of the lexicographic tree such as breadth rst search, depth rst search or a combination of ..."
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Cited by 172 (2 self)
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In this paper we propose algorithms for generation of frequent itemsets by successive construction of the nodes of a lexicographic tree of itemsets. We discuss di erent strategies in generation and traversal of the lexicographic tree such as breadth rst search, depth rst search or a combination of the two. These techniques provide di erent tradeo s in terms of the I/O, memory and computational time requirements. We use the hierarchical structure of the lexicographic tree to successively project transactions at each node of the lexicographic tree, and use matrix counting on this reduced set of transactions for nding frequent itemsets. We tested our algorithm on both real and synthetic data. We provide an implementation of the tree projection method which is up to one order of magnitude faster than other recent techniques in the literature. The algorithm has a well structured data access pattern which provides data locality and reuse of data for multiple levels of the cache. We also discuss methods for parallelization of the
Closet+: searching for the best strategies for mining frequent closed itemsets
, 2003
"... Mining frequent closed itemsets provides complete and nonredundant results for frequent pattern analysis. Extensive studies have proposed various strategies for efficient frequent closed itemset mining, such as depthfirst search vs. breadthfirst search, vertical formats vs. horizontal formats, tree ..."
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Cited by 150 (18 self)
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Mining frequent closed itemsets provides complete and nonredundant results for frequent pattern analysis. Extensive studies have proposed various strategies for efficient frequent closed itemset mining, such as depthfirst search vs. breadthfirst search, vertical formats vs. horizontal formats, treestructure vs. other data structures, topdown vs. bottomup traversal, pseudo projection vs. physical projection of conditional database, etc. It is the right time to ask “what are the pros and cons of the strategies? ” and “what and how can we pick and integrate the best strategies to achieve higher performance in general cases?” In this study, we answer the above questions by a systematic study of the search strategies and develop a winning algorithm CLOSET+. CLOSET+ integrates the advantages of the previously proposed effective strategies as well as some ones newly developed here. A thorough performance study on synthetic and real data sets has shown the advantages of the strategies and the improvement of CLOSET+ over existing mining algorithms, including CLOSET, CHARM and OP, in terms of runtime, memory usage and scalability.
Parallel and Distributed Association Mining: A Survey
 IEEE Concurrency
, 1999
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A DataClustering Algorithm On Distributed Memory Multiprocessors
 In LargeScale Parallel Data Mining, Lecture Notes in Artificial Intelligence
, 2000
"... To cluster increasingly massive data sets that are common today in data and text mining, we propose a parallel implementation of the kmeans clustering algorithm based on the message passing model. The proposed algorithm exploits the inherent dataparallelism in the kmeans algorithm. We analyticall ..."
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Cited by 98 (1 self)
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To cluster increasingly massive data sets that are common today in data and text mining, we propose a parallel implementation of the kmeans clustering algorithm based on the message passing model. The proposed algorithm exploits the inherent dataparallelism in the kmeans algorithm. We analytically show that the speedup and the scaleup of our algorithm approach the optimal as the number of data points increases. We implemented our algorithm on an IBM POWERparallel SP2 with a maximum of 16 nodes. On typical test data sets, we observe nearly linear relative speedups, for example, 15.62 on 16 nodes, and essentially linear scaleup in the size of the data set and in the number of clusters desired. For a 2 gigabyte test data set, our implementation drives the 16 node SP2 at more than 1.8 gigaflops. Keywords: kmeans, data mining, massive data sets, messagepassing, text mining. 1 Introduction Data sets measuring in gigabytes and even terabytes are now quite common in data and text minin...
Clustering Based On Association Rule Hypergraphs
"... Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. These discovered clusters are used to explain the characteristics of the data distribution. In this paper we propose a new metho ..."
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Cited by 93 (17 self)
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Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. These discovered clusters are used to explain the characteristics of the data distribution. In this paper we propose a new methodology for clustering related items using association rules, and clustering related transactions using clusters of items. Our approach is linearly scalable with respect to the number of transactions. The frequent itemsets used to derive association rules are also used to group items into a hypergraph edge, and a hypergraph partitioning algorithm is used to find the clusters. Our experiments indicate that clustering using association rule hypergraphs holds great promise in several application domains. Our experiments with stockmarket data and congressional voting data show that this clustering scheme is able to successfully group items that belong to the same group. Clustering of items can ...
Parallel data mining for association rules on sharedmemory multiprocessors
 In Proc. Supercomputing’96
, 1996
"... Abstract. In this paper we present a new parallel algorithm for data mining of association rules on sharedmemory multiprocessors. We study the degree of parallelism, synchronization, and data locality issues, and present optimizations for fast frequency computation. Experiments show that a signific ..."
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Cited by 75 (19 self)
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Abstract. In this paper we present a new parallel algorithm for data mining of association rules on sharedmemory multiprocessors. We study the degree of parallelism, synchronization, and data locality issues, and present optimizations for fast frequency computation. Experiments show that a significant improvement of performance is achieved using our proposed optimizations. We also achieved good speedup for the parallel algorithm. A lot of datamining tasks (e.g. association rules, sequential patterns) use complex pointerbased data structures (e.g. hash trees) that typically suffer from suboptimal data locality. In the multiprocessor case shared access to these data structures may also result in false sharing. For these tasks it is commonly observed that the recursive data structure is built once and accessed multiple times during each iteration. Furthermore, the access patterns after the build phase are highly ordered. In such cases locality and false sharing sensitive memory placement of these structures can enhance performance significantly. We evaluate a set of placement policies for parallel association discovery, and show that simple placement schemes can improve execution time by more than a factor of two. More complex schemes yield additional gains.
Alternative interest measures for mining associations in databases
 IEEE Transactions on Knowledge and Data Engineering
"... Abstract—Data mining is defined as the process of discovering significant and potentially useful patterns in large volumes of data. Discovering associations between items in a large database is one such data mining activity. In finding associations, support is used as an indicator as to whether an a ..."
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Cited by 69 (0 self)
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Abstract—Data mining is defined as the process of discovering significant and potentially useful patterns in large volumes of data. Discovering associations between items in a large database is one such data mining activity. In finding associations, support is used as an indicator as to whether an association is interesting. In this paper, we discuss three alternative interest measures for associations: anyconfidence, allconfidence, and bond. We prove that the important downward closure property applies to both allconfidence and bond. We show that downward closure does not hold for anyconfidence. We also prove that, if associations have a minimum allconfidence or minimum bond, then those associations will have a given lower bound on their minimum support and the rules produced from those associations will have a given lower bound on their minimum confidence as well. However, associations that have that minimum support (and likewise their rules that have minimum confidence) may not satisfy the minimum allconfidence or minimum bond constraint. We describe the algorithms that efficiently find all associations with a minimum allconfidence or minimum bond and present some experimental results. Index Terms—Data mining, associations, interest measures, databases, performance. æ 1