On detecting differences between groups
Published 24 August 2003
Geoffrey I. Webb, Shane M. Butler, Douglas Newlands
Citations109
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
It is observed that straightforward application of an existing commercial rule-discovery system, Magnum Opus, could successfully perform the contrast-set-mining task, leading to the realization that contrast- set mining is a special case of the more general rule-Discovery task.
Abstract
Understanding the differences between contrasting groups is a fundamental task in data analysis. This realization has led to the development of a new special purpose data mining technique, contrast-set mining. We undertook a study with a retail collaborator to compare contrast-set mining with existing rule-discovery techniques. To our surprise we observed that straightforward application of an existing commercial rule-discovery system, Magnum Opus, could successfully perform the contrast-set-mining task. This led to the realization that contrast-set mining is a special case of the more general rule-discovery task. We present the results of our study together with a proof of this conclusion.
Keywords
Computer Science
C4.5: Programs for Machine Learning
23,665 Citations1992J. R. Quinlan
A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Mining association rules between sets of items in large databases
14,720 Citations1993Rakesh Agrawal, Tomasz Imieliński +1 more
An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Programs for Machine Learning
5,794 Citations1994Steven L. Salzberg, Alberto M. Segre
In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments, which will be a welcome addition to the library of many researchers and students.
Efficiently mining long patterns from databases
1,297 Citations1998Roberto J. Bayardo
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, compared with previous algorithms that scale exponentially with longest pattern length.
Efficient mining of emerging patterns
1,005 Citations1999Guozhu Dong, Jinyan Li
It is believed that EPs with low to medium support, such as 1%-20%, can give useful new insights and guidance to experts, in even “well understood” applications.
Data Mining and Knowledge DiscoveryDetecting Group Differences: Mining Contrast Sets
362 Citations2001Stephen D. Bay, Michael J. Pazzani
This work provides a search algorithm for mining contrast sets with pruning rules that drastically reduce the computational complexity and post-process the results to present a subset that is surprising to the user given what the user has already shown.
ScholarlyCommons (University of Pennsylvania)Search through systematic set enumeration
334 Citations1992Ron Rymon
The Set-Enumerations (SE)tree is presented as a vehicle for representing sets and/or enumerating them in a best-first fashion and its usefulness as the basis for a unifying search-based framework for domains where minimal (maximal) elements of a power set are targeted.
Journal of Artificial Intelligence ResearchOPUS: An Efficient Admissible Algorithm for Unordered Search
209 Citations1995Geoffrey I. Webb
The use of admissible search is of potential value to the machine learning community as it means that the exact learning biases to be employed for complex learning tasks can be precisely specified and manipulated.
Detecting change in categorical data
194 Citations1999Stephen D. Bay, Michael J. Pazzani
An algorithm for mining contrast-sets as well as several pruning rules to reduce the computational complexity are provided and a subset that is surprising to the user given what has already been shown is presented.
Efficient search for association rules
164 Citations2000Geoffrey I. Webb
This paper argues that for some applications direct search for association rules can be more e cient than the tw o stage process of the Apriori algorithm, which can impose large computational overheads when the number of frequen titemsets is very large.
Brute-force mining of high-confidence classification rules
105 Citations1997Jr. Roberto J. Bayardo
An association rule miner enhanced with new pruning strategies to control combinatorial explosion in the number of candidates counted with each database pass effectively and efficiently extracts high confidence classification rules that apply to most if not all of the data in several classification benchmarks.
[1990] Proceedings of the 2nd International IEEE Conference on Tools for Artificial IntelligenceRL4: a tool for knowledge-based induction
99 Citations2002Scott H. Clearwater, Foster Provost
The induction program RL4 is used as an induction tool, and several examples of its past and present uses are presented, and the directions in which RL4 can go in the future are considered.
Learning decision lists using homogeneous rules
48 Citations1994Richard Segal, Oren Etzioni
It is proved that the problem of finding a maximally accurate decision list can be reduced to the problemOf finding maximally inaccurate homogeneous rules, rules whose classification accuracy does not change with their position in the decision list.