Large and complex graphs representing relationships among sets of entities are an increasingly common focus of interest in data analysis--examples include social networks, Web gra...
We describe techniques for combining two types of knowledge systems: expert and machine learning. Both the expert system and the learning system represent information by logical d...
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...
Geoffrey I. Webb, Shane M. Butler, Douglas A. Newl...
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...
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...
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...
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 ...
Data Mining with Bayesian Network learning has two important characteristics: under broad conditions learned edges between variables correspond to causal influences, and second, f...
Ioannis Tsamardinos, Constantin F. Aliferis, Alexa...