With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive ...
Frequent itemset mining is a core data mining operation and has been extensively studied over the last decade. This paper takes a new approach for this problem and makes two major...
Collaborative filtering-based recommender systems, which automatically predict preferred products of a user using known preferences of other users, have become extremely popular ...
We present CoLe, a cooperative data mining approach for discovering hybrid knowledge. It employs multiple different data mining algorithms, and combines results from them to enhan...
Privacy consideration has much significance in the application of data mining. It is very important that the privacy of individual parties will not be exposed when data mining te...
Sampling has been recognized as an important technique to improve the efficiency of clustering. However, with sampling applied, those points which are not sampled will not have t...
It is estimated that ninety percent of the world’s species have yet to be discovered and described. The main reason for the slow pace of new species description is that the scie...
Yixin Chen, Henry L. Bart Jr., Shuqing Huang, Huim...
There has been increasing number of independently proposed randomization methods in different stages of decision tree construction to build multiple trees. Randomized decision tre...
Wei Fan, Ed Greengrass, Joe McCloskey, Philip S. Y...
In this paper, we formulate the problem of summarization of a dataset of transactions with categorical attributes as an optimization problem involving two objective functions - co...
Significant vulnerabilities have recently been identified in collaborative filtering recommender systems. Researchers have shown that attackers can manipulate a system’s reco...
Robin D. Burke, Bamshad Mobasher, Runa Bhaumik, Ch...