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Mining Generalized Association Rules

by Ramakrishnan Srikant, Rakesh Agrawal , 1995
"... We introduce the problem of mining generalized association rules. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy. For example, given a taxonomy th ..."
Abstract - Cited by 577 (7 self) - Add to MetaCart
solution to the problem is to add all ancestors of each item in a transaction to the transaction, and then run any of the algorithms for mining association rules on these "extended transactions ". However, this "Basic" algorithm is not very fast; we present two algorithms, Cumulate

An efficient algorithm for mining association rules in large databases

by Ashok Savasere, Edward Omiecinski, Shamkant Navathe , 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 effi-cient algorithm for mining association rules that is fundamentally different from known al-gorithms. Compared to previous ..."
Abstract - Cited by 431 (0 self) - Add to MetaCart
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 effi-cient algorithm for mining association rules that is fundamentally different from known al-gorithms. Compared

Fast Algorithms for Mining Association Rules

by Rakesh Agrawal, Ramakrishnan Srikant , 1994
"... We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known a ..."
Abstract - Cited by 3551 (15 self) - Add to MetaCart
We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known

Integrating classification and association rule mining

by Bing Liu, Wynne Hsu, Yiming Ma - In Proc of KDD , 1998
"... Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target of di ..."
Abstract - Cited by 561 (21 self) - Add to MetaCart
Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target

Mining Association Rules between Sets of Items in Large Databases

by Rakesh Agrawal, Tomasz Imielinski, Arun Swami - IN: PROCEEDINGS OF THE 1993 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, WASHINGTON DC (USA , 1993
"... We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel esti ..."
Abstract - Cited by 3260 (17 self) - Add to MetaCart
We are given a large database of customer transactions. Each transaction consists of items purchased by a customer in a visit. We present an efficient algorithm that generates all significant association rules between items in the database. The algorithm incorporates buffer management and novel

Beyond Market Baskets: Generalizing Association Rules To Dependence Rules

by Craig Silverstein, SERGEY BRIN , RAJEEV MOTWANI , 1998
"... One of the more well-studied problems in data mining is the search for association rules in market basket data. Association rules are intended to identify patterns of the type: “A customer purchasing item A often also purchases item B. Motivated partly by the goal of generalizing beyond market bask ..."
Abstract - Cited by 623 (6 self) - Add to MetaCart
One of the more well-studied problems in data mining is the search for association rules in market basket data. Association rules are intended to identify patterns of the type: “A customer purchasing item A often also purchases item B. Motivated partly by the goal of generalizing beyond market

Efficiently mining long patterns from databases

by Roberto J. Bayardo , 1998
"... We present a pattern-mining 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 ..."
Abstract - Cited by 465 (3 self) - Add to MetaCart
We present a pattern-mining 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

An Efficient Boosting Algorithm for Combining Preferences

by Raj Dharmarajan Iyer , Jr. , 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
Abstract - Cited by 707 (18 self) - Add to MetaCart
The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new

Mining Quantitative Association Rules in Large Relational Tables

by Ramakrishnan Srikant, Rakesh Agrawal , 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 ..."
Abstract - Cited by 438 (3 self) - Add to MetaCart
"greater-than-expected-value" 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 real-life dataset. 1 Introduction Data mining, also known

Fast Effective Rule Induction

by William W. Cohen , 1995
"... Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems. We show that while IREP is extremely efficient, it frequently gives error r ..."
Abstract - Cited by 1257 (21 self) - Add to MetaCart
Many existing rule learning systems are computationally expensive on large noisy datasets. In this paper we evaluate the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems. We show that while IREP is extremely efficient, it frequently gives error
Results 1 - 10 of 1,553,738