Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
5,252
Integrating classification and association rule mining
- 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
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 effi-cient algorithm for mining association rules that is fundamentally different from known al-gorithms. Compared to previous ..."
Abstract
-
Cited by 437 (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
, 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
Mining Generalized Association Rules
, 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
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
Mining Association Rules between Sets of Items in Large Databases
- 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
Beyond Market Baskets: Generalizing Association Rules To Dependence Rules
, 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
CURE: An Efficient Clustering Algorithm for Large Data sets
- Published in the Proceedings of the ACM SIGMOD Conference
, 1998
"... Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes, or are very fragile in the presence of outliers. We propose a new clustering ..."
Abstract
-
Cited by 722 (5 self)
- Add to MetaCart
Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes, or are very fragile in the presence of outliers. We propose a new
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 ..."
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
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 ..."
Abstract
-
Cited by 444 (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
Dynamic Itemset Counting and Implication Rules for Market Basket Data
, 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
5,252
