Titlebook: Data Mining and Knowledge Discovery via Logic-Based Methods; Theory, Algorithms, Evangelos Triantaphyllou Book 2010 Springer Science+Busin

Essential

Edge Exploration of Temporal Graphsexpressed in the form ofassociation rules. Such information may have tremendous potential on the marketing of new or existing products. This is the kind of approach used by many enterprises (such as Amazon.com for instance) to recommend new or existing products to their customers. Mining ofassociati

防御

大量

Mining of Association Rulesexpressed in the form ofassociation rules. Such information may have tremendous potential on the marketing of new or existing products. This is the kind of approach used by many enterprises (such as Amazon.com for instance) to recommend new or existing products to their customers. Mining ofassociati

troponins

评论性

Oversee

An Approach to Guided Learning of Boolean Functions, a Boolean function in CNF or DNF form that satisfies the requirements of the positive and negative examples as described in Chapters 2 and 3. It is hoped at this point that the inferred Boolean function will accurately classify all remaining examples not included in the currently available positive and negative examples.

使人烦燥

An Incremental Learning Algorithm for Inferring Boolean Functions the need for an update, one had to use all the existing training examples, plus the newly classified example, to infer a new (and hopefully more accurate) pattern in the form of a Boolean function or other data mining model.

凹槽

The Reliability Issue in Data Mining: The Case of Computer-Aided Breast Cancer Diagnosiso rates: thefalse-positive andfalse-negative rates. This distinction might be more appropriate for most real-life applications. For instance, it is one thing to wrongly diagnose a benign tumor as malignant than the other way around. Related are some of the discussions in Sections 1.3.4, 4.5, and 11.6.

OPINE

Data Mining of Text Documentssets of training examples (text documents) are assumed to be available. An approach is developed that usesindexing terms to form patterns of logical expressions (Boolean functions) that next are used to classify new text documents (which are of unknown class). This is a typical case ofsupervised “crisp” classification.