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6,483
Efficiently mining long patterns from databases
, 1998
"... We present a patternmining algorithm that scales roughly linearly in the number of maximal patterns embedded in a database irrespective of the length of the longest pattern. In comparison, previous algorithms based on Apriori scale exponentially with longest pattern length. Experiments on real data ..."
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Cited by 465 (3 self)
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data show that when the patterns are long, our algorithm is more efficient by an order of magnimaximal frequent itemset, MaxMinerâ€™s output implicitly and concisely represents all frequent itemsets. MaxMiner is shown to result in two or more orders of magnitude in performance improvements over Apriori
An effective hashbased algorithm for mining association rules
, 1995
"... In this paper, we examine the issue of mining association rules among items in a large database of sales transactions. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items which appear in a sufficient number of transac ..."
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Cited by 278 (3 self)
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for the candidate set generation. Explicitly, the number of candidate 2itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck. Note that the generation of smaller candidate sets enables us to effectively trim
Sampling Large Databases for Association Rules
, 1996
"... Discovery of association rules is an important database mining problem. Current algorithms for nding association rules require several passes over the analyzed database, and obviously the role of I/O overhead is very signi cant for very large databases. We present new algorithms that reduce the data ..."
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Cited by 465 (4 self)
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Discovery of association rules is an important database mining problem. Current algorithms for nding association rules require several passes over the analyzed database, and obviously the role of I/O overhead is very signi cant for very large databases. We present new algorithms that reduce the database activity considerably. Theidea is to pick a random sample, to ndusingthis sample all association rules that probably hold in the whole database, and then to verify the results with the restofthe database. The algorithms thus produce exact association rules, not approximations based on a sample. The approach is, however, probabilistic, and inthose rare cases where our sampling method does not produce all association rules, the missing rules can be found inasecond pass. Our experiments show that the proposed algorithms can nd association rules very e ciently in only onedatabase pass. 1
An efficient algorithm for mining association rules in large databases
, 1995
"... Mining for a.ssociation rules between items in a large database of sales transactions has been described as an important database mining problem. In this paper we present an efficient algorithm for mining association rules that is fundamentally different from known algorithms. Compared to previous ..."
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Cited by 431 (0 self)
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Mining for a.ssociation rules between items in a large database of sales transactions has been described as an important database mining problem. In this paper we present an efficient algorithm for mining association rules that is fundamentally different from known algorithms. Compared to previous algorithms, our algorithm not only reduces the I/O overhead significantly but also has lower CPU overhead for most cases. We have performed extensive experiments and compared the performance of our algorithm with one of the best existing algorithms. It was found that for large databases, the CPU overhead was reduced by as much as a factor of four and I/O was reduced by almost an order of magnitude. Hence this algorithm is especially suitable for very large size databases. 1
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 438 (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
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 417 (13 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
ROCK: A Robust Clustering Algorithm for Categorical Attributes
 In Proc.ofthe15thInt.Conf.onDataEngineering
, 2000
"... Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than point ..."
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Cited by 430 (2 self)
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Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than points in different partitions. In this paper, we study clustering algorithms for data with boolean and categorical attributes. We show that traditional clustering algorithms that use distances between points for clustering are not appropriate for boolean and categorical attributes. Instead, we propose a novel concept of links to measure the similarity/proximity between a pair of data points. We develop a robust hierarchical clustering algorithm ROCK that employs links and not distances when merging clusters.
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 407 (14 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
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 ..."
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 391 (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
Results 11  20
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