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Mining Quantitative Association Rules in Large Relational Tables
, 1996
"... We introduce the problem of mining association rules in large relational tables containing both quantitative and categorical attributes. An example of such an association might be "10% of married people between age 50 and 60 have at least 2 cars". We deal with quantitative attributes by fi ..."
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Cited by 444 (3 self)
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We introduce the problem of mining association rules in large relational tables containing both quantitative and categorical attributes. An example of such an association might be "10% of married people between age 50 and 60 have at least 2 cars". We deal with quantitative attributes by finepartitioning the values of the attribute and then combining adjacent partitions as necessary. We introduce measures of partial completeness which quantify the information lost due to partitioning. A direct application of this technique can generate too many similar rules. We tackle this problem by using a "greaterthanexpectedvalue" interest measure to identify the interesting rules in the output. We give an algorithm for mining such quantitative association rules. Finally, we describe the results of using this approach on a reallife dataset. 1 Introduction Data mining, also known as knowledge discovery in databases, has been recognized as a new area for database research. The problem of discove...
Mining version histories to guide software changes
 In 26th International Conference on Software Engineering (ICSE 2004
, 2004
"... We apply data mining to version histories in order to guide programmers along related changes: “Programmers who changed these functions also changed... ”. Given a set of existing changes, such rules (a) suggest and predict likely further changes, (b) show up item coupling that is indetectable by pro ..."
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Cited by 422 (25 self)
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We apply data mining to version histories in order to guide programmers along related changes: “Programmers who changed these functions also changed... ”. Given a set of existing changes, such rules (a) suggest and predict likely further changes, (b) show up item coupling that is indetectable by program analysis, and (c) prevent errors due to incomplete changes. After an initial change, our ROSE prototype can correctly predict 26 % of further files to be changed—and 15 % of the precise functions or variables. The topmost three suggestions contain a correct location with a likelihood of 64%. 1.
Approximate Frequency Counts over Data Streams
 VLDB
, 2002
"... We present algorithms for computing frequency counts exceeding a userspecified threshold over data streams. Our algorithms are simple and have provably small memory footprints. Although the output is approximate, the error is guaranteed not to exceed a userspecified parameter. Our algorithms can e ..."
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Cited by 418 (1 self)
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We present algorithms for computing frequency counts exceeding a userspecified threshold over data streams. Our algorithms are simple and have provably small memory footprints. Although the output is approximate, the error is guaranteed not to exceed a userspecified parameter. Our algorithms can easily be deployed for streams of singleton items like those found in IP network monitoring. We can also handle streams of variable sized sets of items exemplified by a sequence of market basket transactions at a retail store. For such streams, we describe an optimized implementation to compute frequent itemsets in a single pass.
Discovering Frequent Closed Itemsets for Association Rules
, 1999
"... In this paper, we address the problem of finding frequent itemsets in a database. Using the closed itemset lattice framework, we show that this problem can be reduced to the problem of finding frequent closed itemsets. Based on this statement, we can construct efficient data mining algorithms by lim ..."
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Cited by 410 (14 self)
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In this paper, we address the problem of finding frequent itemsets in a database. Using the closed itemset lattice framework, we show that this problem can be reduced to the problem of finding frequent closed itemsets. Based on this statement, we can construct efficient data mining algorithms by limiting the search space to the closed itemset lattice rather than the subset lattice. Moreover, we show that the set of all frequent closed itemsets suffices to determine a reduced set of association rules, thus addressing another important data mining problem: limiting the number of rules produced without information loss. We propose a new algorithm, called AClose, using a closure mechanism to find frequent closed itemsets. We realized experiments to compare our approach to the commonly used frequent itemset search approach. Those experiments showed that our approach is very valuable for dense and/or correlated data that represent an important part of existing databases.
Frequent Subgraph Discovery
, 2001
"... Over the years, frequent itemset discovery algorithms have been used to solve various interesting problems. As data mining techniques are being increasingly applied to nontraditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of th ..."
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Cited by 406 (10 self)
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Over the years, frequent itemset discovery algorithms have been used to solve various interesting problems. As data mining techniques are being increasingly applied to nontraditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets, is to use a graph to model the database objects. Within that model, the problem of finding frequent patterns becomes that of discovering subgraphs that occur frequently over the entire set of graphs. In this paper we present a computationally efficient algorithm for finding all frequent subgraphs in large graph databases. We evaluated the performance of the algorithm by experiments with synthetic datasets as well as a chemical compound dataset. The empirical results show that our algorithm scales linearly with the number of input transactions and it is able to discover frequent subgraphs from a set of graph transactions reasonably fast, even though we have to deal with computationally hard problems such as canonical labeling of graphs and subgraph isomorphism which are not necessary for traditional frequent itemset discovery.
New Algorithms for Fast Discovery of Association Rules
 In 3rd Intl. Conf. on Knowledge Discovery and Data Mining
, 1997
"... 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 397 (26 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 related database items are grouped together into clusters representing the potential maximal frequent itemsets in the database. Each cluster induces a sublattice of the itemset lattice. Efficient lattice traversal techniques are presented, which quickly identify all the true maximal frequent itemsets, and all their subsets if desired. We also present the effect of using different database layout schemes combined with the proposed clustering and traversal techniques. The proposed algorithms scan a (preprocessed) d...
The Web as a graph: measurements, models, and methods
, 1999
"... . The pages and hyperlinks of the WorldWide Web may be viewed as nodes and edges in a directed graph. This graph is a fascinating object of study: it has several hundred million nodes today, over a billion links, and appears to grow exponentially with time. There are many reasons  mathematical, ..."
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Cited by 373 (11 self)
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. The pages and hyperlinks of the WorldWide Web may be viewed as nodes and edges in a directed graph. This graph is a fascinating object of study: it has several hundred million nodes today, over a billion links, and appears to grow exponentially with time. There are many reasons  mathematical, sociological, and commercial  for studying the evolution of this graph. In this paper we begin by describing two algorithms that operate on the Web graph, addressing problems from Web search and automatic community discovery. We then report a number of measurements and properties of this graph that manifested themselves as we ran these algorithms on the Web. Finally, we observe that traditional random graph models do not explain these observations, and we propose a new family of random graph models. These models point to a rich new subfield of the study of random graphs, and raise questions about the analysis of graph algorithms on the Web. 1 Overview Few events in the history of comput...
Mining: Information and Pattern Discovery on the World Wide Web
 In: Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI
, 1997
"... Application of data mining techniques to the World Wide Web, referred to as Web mining, has been the focus of several recent research projects and papers. However, there is no established vocabulary, leading to confusion when comparing research efforts. The term Web mining has been used in two disti ..."
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Cited by 372 (21 self)
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Application of data mining techniques to the World Wide Web, referred to as Web mining, has been the focus of several recent research projects and papers. However, there is no established vocabulary, leading to confusion when comparing research efforts. The term Web mining has been used in two distinct ways. The first, called Web content mining in this paper, is the process of information discovery from sources across the World Wide Web. The second, called Web mage mining, is the process of mining for user browsing and access patterns. In this paper we define Web mining and present an overview of the various research issues, techniques, and development efforts. We briefly describe WEBMINER, a system for Web usage mining, and conclude this paper by listing research issues. 1
Trawling the Web for emerging cybercommunities
 Computer Networks
, 1999
"... Abstract: The web harbors a large number of communities groups of contentcreators sharing a common interest each of which manifests itself as a set of interlinked web pages. Newgroups and commercial web directories together contain of the order of 20000 such communities; our particular interest ..."
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Cited by 369 (8 self)
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Abstract: The web harbors a large number of communities groups of contentcreators sharing a common interest each of which manifests itself as a set of interlinked web pages. Newgroups and commercial web directories together contain of the order of 20000 such communities; our particular interest here is on emerging communities those that have little or no representation in such fora. The subject of this paper is the systematic enumeration of over 100,000 such emerging communities from a web crawl: we call our process trawling. We motivate a graphtheoretic approach to locating such communities, and describe the algorithms, and the algorithmic engineering necessary to find structures that subscribe to this notion, the challenges in handling such a huge data set, and the results of our experiment.