Results 1 - 10 of 40,363

Data Mining: An Overview from Database Perspective

by Ming-syan Chen, Jiawei Hun, Philip S. Yu - IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 1996
"... Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many different fields have sh ..."
Abstract - Cited by 532 (26 self) - Add to MetaCart
Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many different fields have

From Data Mining to Knowledge Discovery in Databases.

by Usama Fayyad , Gregory Piatetsky-Shapiro , Padhraic Smyth - AI Magazine, , 1996
"... ■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in database ..."
Abstract - Cited by 538 (0 self) - Add to MetaCart
■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery

PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth

by Jian Pei, Jiawei Han, Behzad Mortazavi-asl, Helen Pinto, Qiming Chen, Umeshwar Dayal, Mei-chun Hsu , 2001
"... Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology of ..."
Abstract - Cited by 347 (27 self) - Add to MetaCart
Sequential pattern mining is an important data mining problem with broad applications. It is challenging since one may need to examine a combinatorially explosive number of possible subsequence patterns. Most of the previously developed sequential pattern mining methods follow the methodology

Mining Sequential Patterns

by Rakesh Agrawal, Ramakrishnan Srikant , 1995
"... We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empiri ..."
Abstract - Cited by 1568 (6 self) - Add to MetaCart
We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem

Data Preparation for Mining World Wide Web Browsing Patterns

by Robert Cooley, Bamshad Mobasher, Jaideep Srivastava - KNOWLEDGE AND INFORMATION SYSTEMS , 1999
"... The World Wide Web (WWW) continues to grow at an astounding rate in both the sheer volume of tra#c and the size and complexity of Web sites. The complexity of tasks such as Web site design, Web server design, and of simply navigating through a Web site have increased along with this growth. An i ..."
Abstract - Cited by 567 (43 self) - Add to MetaCart
. An important input to these design tasks is the analysis of how a Web site is being used. Usage analysis includes straightforward statistics, such as page access frequency, as well as more sophisticated forms of analysis, such as finding the common traversal paths through a Web site. Web Usage Mining

Mining Sequential Patterns: Generalizations and Performance Improvements

by Ramakrishnan Srikant, Rakesh Agrawal - RESEARCH REPORT RJ 9994, IBM ALMADEN RESEARCH , 1995
"... The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user-specified ..."
Abstract - Cited by 759 (5 self) - Add to MetaCart
The problem of mining sequential patterns was recently introduced in [3]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user

SPRINT: A scalable parallel classifier for data mining

by John Shafer, Rakeeh Agrawal, Manish Mehta , 1996
"... Classification is an important data mining problem. Although classification is a well-studied problem, most of the current classi-fication algorithms require that all or a por-tion of the the entire dataset remain perma-nently in memory. This limits their suitability for mining over large databases. ..."
Abstract - Cited by 312 (8 self) - Add to MetaCart
Classification is an important data mining problem. Although classification is a well-studied problem, most of the current classi-fication algorithms require that all or a por-tion of the the entire dataset remain perma-nently in memory. This limits their suitability for mining over large databases

Dynamic Itemset Counting and Implication Rules for Market Basket Data

by Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, Shalom Tsur , 1997
"... We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We in ..."
Abstract - Cited by 615 (6 self) - Add to MetaCart
We consider the problem of analyzing market-basket data and present several important contributions. First, we present a new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling. We

Data Integration: A Theoretical Perspective

by Maurizio Lenzerini - Symposium on Principles of Database Systems , 2002
"... Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interestin ..."
Abstract - Cited by 965 (45 self) - Add to MetaCart
Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues

Discovering Frequent Closed Itemsets for Association Rules

by Nicolas Pasquier, Yves Bastide, Rafik Taouil, Lotfi Lakhal , 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 ..."
Abstract - Cited by 410 (14 self) - Add to MetaCart
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
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