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A Tool for Extracting XML Association Rules from XML Documents

by Daniele Braga, Ro Campi, Stefano Ceri, Mika Klemettinen, Pier Luca Lanzi
"... The recent success of XML as a standard to represent semi-structured data, and the increasing amount of available XML data, pose new challenges to the data mining community. In this paper we present the XMINE operator a tool we developed to extract XML association rules for XML documents. The operat ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
The recent success of XML as a standard to represent semi-structured data, and the increasing amount of available XML data, pose new challenges to the data mining community. In this paper we present the XMINE operator a tool we developed to extract XML association rules for XML documents

A New Model for Discovering XML Association Rules from XML Documents

by unknown authors
"... Abstract—The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized associa ..."
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Abstract—The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized

A New Model for Discovering XML Association Rules from XML Documents

by R. AliMohammadzadeh, M. Rahgozar, A. Zarnani - TRANSACTIONS ON ENGINEERING, COMPUTING AND TECHNOLOGY , 2006
"... Abstract—The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized associa ..."
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Abstract—The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized

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 591 (7 self) - Add to MetaCart
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

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 578 (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

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 3612 (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

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 634 (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

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 3331 (16 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

Sampling Large Databases for Association Rules

by Hannu Toivonen , 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 ..."
Abstract - Cited by 470 (3 self) - Add to MetaCart
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

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
investigate the idea of item reordering, which can improve the low-level efficiency of the algorithm. Second, we present a new way of generating "implication rules," which are normalized based on both the antecedent and the consequent and are truly implications (not simply a measure of co
Results 1 - 10 of 16,212