Users prefer to navigate subjects from organized topics in an abundance resources than to list pages retrieved from search engines. We propose a framework to cluster frequent items...
In earlier work we have introduced and explored a variety of different probabilistic models for the problem of answering selectivity queries posed to large sparse binary data set...
Background: Supervised learning for classification of cancer employs a set of design examples to learn how to discriminate between tumors. In practice it is crucial to confirm tha...
Abstract. Accurate selectivity estimations are essential for query optimization decisions where they are typically derived from various kinds of histograms which condense value dis...
Data mining aims at extraction of previously unidentified information from large databases. It can be viewed as an automated application of algorithms to discover hidden patterns a...