170
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KDD
2003
ACM
16 years 7 months ago
2003
ACM
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 t...
142
Voted
KDD
2003
ACM
16 years 7 months ago
2003
ACM
Unexpected rules are interesting because they are either previously unknown or deviate from what prior user knowledge would suggest. In this paper, we study three important issues...
198
click to vote
KDD
2003
ACM
16 years 7 months ago
2003
ACM
Recently, mining data streams with concept drifts for actionable insights has become an important and challenging task for a wide range of applications including credit card fraud...
180
click to vote
KDD
2003
ACM
16 years 7 months ago
2003
ACM
Although most time-series data mining research has concentrated on providing solutions for a single distance function, in this work we motivate the need for a single index structu...
170
click to vote
KDD
2003
ACM
16 years 7 months ago
2003
ACM
Privacy and security concerns can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. The key ...
173
click to vote
KDD
2003
ACM
16 years 7 months ago
2003
ACM
Data Mining with Bayesian Network learning has two important characteristics: under broad conditions learned edges between variables correspond to causal influences, and second, f...
177
click to vote
KDD
2003
ACM
16 years 7 months ago
2003
ACM
Decision trees are commonly used for classification. We propose to use decision trees not just for classification but also for the wider purpose of knowledge discovery, because vi...
219
click to vote
KDD
2003
ACM
16 years 7 months ago
2003
ACM
We address the issues of discovering significant binary relationships in transaction datasets in a weighted setting. Traditional model of association rule mining is adapted to han...
221
click to vote
KDD
2003
ACM
16 years 7 months ago
2003
ACM
The goal of clustering is to identify distinct groups in a dataset. The basic idea of model-based clustering is to approximate the data density by a mixture model, typically a mix...