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634
Mining Frequent Patterns without Candidate Generation: A FrequentPattern Tree Approach
 DATA MINING AND KNOWLEDGE DISCOVERY
, 2004
"... Mining frequent patterns in transaction databases, timeseries databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriorilike candidate set generationandtest approach. However, candidate set generation is still co ..."
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Cited by 1752 (64 self)
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Mining frequent patterns in transaction databases, timeseries databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriorilike candidate set generationandtest approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns. In this study, we propose a novel
frequentpattern tree
(FPtree) structure, which is an extended prefixtree
structure for storing compressed, crucial information about frequent patterns, and develop an efficient FPtree
based mining method, FPgrowth, 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 condensed,
smaller data structure, FPtree which avoids costly, repeated database scans, (2) our FPtreebased mining adopts
a patternfragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a
partitioningbased, divideandconquer 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 FPgrowth 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 frequentpattern mining methods
Agglomerative Clustering of a Search Engine Query Log
 In Proceedings of the sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2000
"... This paper introduces a technique for mining a collection of user transactions with an Internet search engine to discover clusters of similar queries and similar URLs. The information we exploit is "clickthrough data": each record consists of a user's query to a search engine along wi ..."
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Cited by 330 (0 self)
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This paper introduces a technique for mining a collection of user transactions with an Internet search engine to discover clusters of similar queries and similar URLs. The information we exploit is "clickthrough data": each record consists of a user's query to a search engine along with the URL which the user selected from among the candidates offered by the search engine. By viewing this dataset as a bipartite graph, with the vertices on one side corresponding to queries and on the other side to URLs, one can apply an agglomerative clustering algorithm to the graph's vertices to identify related queries and URLs. One noteworthy feature of the proposed algorithm is that it is "contentignorant"the algorithm makes no use of the actual content of the queries or URLs, but only how they cooccur within the clickthrough data. We describe how to enlist the discovered clusters to assist users in web search, and measure the effectiveness of the discovered clusters in the Lycos search engine...
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 313 (44 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.
Levelwise Search and Borders of Theories in Knowledge Discovery
, 1997
"... One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for defining subgroups of r, and a selection predicate, find all sentences of L deemed interesting by the selection predicate. We analyze the simple levelwise algorithm fo ..."
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Cited by 263 (15 self)
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One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for defining subgroups of r, and a selection predicate, find all sentences of L deemed interesting by the selection predicate. We analyze the simple levelwise algorithm for finding all such descriptions. We give bounds for the number of database accesses that the algorithm makes. For this, we introduce the concept of the border of a theory, a notion that turns out to be surprisingly powerful in analyzing the algorithm. We also consider the verification problem of a KDD process: given r and a set of sentences S ` L, determine whether S is exactly the set of interesting statements about r. We show strong connections between the verification problem and the hypergraph transversal problem. The verification problem arises in a natural way when using sampling to speed up the pattern discovery step in KDD.
Selecting the Right Interestingness Measure for Association Patterns
, 2002
"... Many techniques for association rule mining and feature selection require a suitable metric to capture the dependencies among variables in a data set. For example, metrics such as support, confidence, lift, correlation, and collective strength are often used to determine the interestinghess of assoc ..."
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Cited by 254 (10 self)
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Many techniques for association rule mining and feature selection require a suitable metric to capture the dependencies among variables in a data set. For example, metrics such as support, confidence, lift, correlation, and collective strength are often used to determine the interestinghess of association patterns. However, many such measures provide conflicting information about the interestinghess of a pattern, and the best metric to use for a given application domain is rarely known. In this paper, we present an overview of various measures proposed in the statistics, machine learning and data mining literature. We describe several key properties one should examine in order to select the right measure for a given application domain. A comparative study of these properties is made using twenty one of the existing measures. We show that each measure has different properties which make them useful for some application domains, but not for others. We also present two scenarios in which most of the existing measures agree with each other, namely, supportbased pruning and table standardization. Finally, we present an algorithm to select a small set of tables such that an expert can select a desirable measure by looking at just this small set of tables.
Rule discovery from time series
 In Proceedings of the 1997 ACM SIGKDD International Conference, ACM SIGKDD
, 1997
"... We consider the problem of finding rules relating patterns in a time series to other patterns in that series, or patterns in one series to patterns in another series. A simple example is a rule such as "a period of low telephone call activity is usually followed by a sharp rise ill call vohune& ..."
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Cited by 181 (0 self)
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We consider the problem of finding rules relating patterns in a time series to other patterns in that series, or patterns in one series to patterns in another series. A simple example is a rule such as "a period of low telephone call activity is usually followed by a sharp rise ill call vohune". Examples of rules relating two or more time series are "if the Microsoft stock price goes up and lntel falls, then IBM goes up the next. day, " and "if Microsoft goes up strongly fro " one day, then declines strongly on the next day, and on the same days Intel stays about, level, then IBM stays about level. " Our emphasis is in the discovery of local patterns in multivariate time series, in contrast to traditional time series analysis which largely focuses on global models. Thus, we search for rules whose conditions refer to patterns in time series. However, we do not want to define beforehand which patterns are to be used; rather, we want the patterns to be formed fl’om the data in the context of rule discovery. We describe adaptive methods for finding rules of the above type fi’om timeseries data. The methods are based on discretizing the sequence hy methods resembling vector quantization. \,Ve first form subsequences by sliding window through the time series, and then cluster these subsequences by using a suitable measure of timeseries similarity. The discretized version of the time series is obtained by taldng the cluster identifiers corresponding to the subsequence. Once tl,e timeseries is discretized, we use simple rule finding methods to obtain rifles from the sequence. "vVe present empMcal resuh.s on the behavior of the method.
Predicting source code changes by mining change history
 IEEE Transactions on Software Engineering
"... ©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted ..."
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Cited by 168 (2 self)
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©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted
An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback
 In proceedings of the 4th Int'l Conference on Knowledge Discovery and Data Mining
"... We introduce an extended representation of time series that allows fast, accurate classification and clustering in addition to the ability to explore time series data in a relevance feedback framework. The representation consists of piecewise linear segments to represent shape and a weight vector th ..."
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Cited by 165 (25 self)
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We introduce an extended representation of time series that allows fast, accurate classification and clustering in addition to the ability to explore time series data in a relevance feedback framework. The representation consists of piecewise linear segments to represent shape and a weight vector that contains the relative importance of each individual linear segment. In the classification context, the weights are learned automatically as part of the training cycle. In the relevance feedback context, the weights are determined by an interactive and iterative process in which users rate various choices presented to them. Our representation allows a user to define a variety of similarity measures that can be tailored to specific domains. We demonstrate our approach on space telemetry, medical and synthetic data.
Efficient Mining Of Association Rules Using Closed Itemset Lattices
 INFORMATION SYSTEMS
, 1999
"... Discovering association rules is one of the most important task in data mining. Many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset ..."
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Cited by 163 (12 self)
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Discovering association rules is one of the most important task in data mining. Many efficient algorithms have been proposed in the literature. The most noticeable are Apriori, Mannila's algorithm, Partition, Sampling and DIC, that are all based on the Apriori mining method: pruning the subset lattice (itemset lattice). In this paper we propose an efficient algorithm, called Close, based on a new mining method: pruning the closed set lattice (closed itemset lattice). This lattice, which is a suborder of the subset lattice, is closely related to Wille's concept lattice in formal concept analysis. Experiments comparing Close to an optimized version of Apriori showed that Close is very efficient for mining dense and/or correlated data such as census style data, and performs reasonably well for market basket style data.
Entropybased Subspace Clustering for Mining Numerical Data
, 1999
"... Mining numerical data is a relatively difficult problem in data mining. Clustering is one of the techniques. We consider a database with numerical attributes, in which each transaction is viewed as a multidimensional vector. By studying the clusters formed by these vectors, we can discover certain ..."
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Cited by 162 (1 self)
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Mining numerical data is a relatively difficult problem in data mining. Clustering is one of the techniques. We consider a database with numerical attributes, in which each transaction is viewed as a multidimensional vector. By studying the clusters formed by these vectors, we can discover certain behaviors hidden in the data. Traditional clustering algorithms find clusters in the full space of the data sets. This results in high dimensional clusters, which are poorly comprehensible to human. One important task in this setting is the ability to discover clusters embedded in the subspaces of a highdimensional data set. This problem is known as subspace clustering. We follow the basic assumptions of previous work CLIQUE. It is found that the number of subspaces with clustering is very large, and a criterion called the coverage is proposed in CLIQUE for the pruning. In addition to coverage, we identify new useful criteria for this problem and propose an entropybased algorithm called ENC...