Data Mining Gary

A Novel Data Mining Methodology for Narrative Text Mining and Its Application in MSHA Accident, Injury and Illness Database

Detecting a Spectrally Variable Subject in Color Infrared Imagery Using Data - Mining and Knowledge - Engine Methods

To classify Egeria densa, Brazilian waterweed, in scan-digitized color infrared aerial photographs, we are developing automated methods based on data-mining and knowledge-engine techniques. In this paper, we present progress to date, compare the results of the two approaches, and discuss current problems and anticipated solutions.

Mining with Rare Cases

Rare cases are often the most interesting cases. For example, in medical diagnosis one is typically interested in identifying relatively rare diseases, such as cancer, rather than more frequently occurring ones, such as the common cold. In this chapter we discuss the role of rare cases in data mining. Specific problems associated with mining rare cases are discussed, followed by a description o...

Data Mining in Telecommunications

Telecommunication companies generate a tremendous amount of data. These data include call detail data, which describes the calls that traverse the telecommunication networks, network data, which describes the state of the hardware and software components in the network, and customer data, which describes the telecommunication customers. This chapter describes how data mining can be used to unco...

Data Preparation for Data Mining

Senior Editor: Diane D. Cerra Director of Production & Manufacturing: Yonie Overton Production Editor: Edward Wade Editorial Assistant: Belinda Breyer Cover Design: Wall-To-Wall Studios Cover Photograph: © 1999 PhotoDisc, Inc. Text Design & Composition: Rebecca Evans & Associates Technical Illustration: Dartmouth Publishing, Inc. Copyeditor: Gary Morris Proofreader: Ken DellaPenta Indexer: Stev...

Teaching Financial Data Mining using Stocks and Futures Contracts

Financial data mining models is considered to be “the hardest way to make easy money.” Data miners are certainly motivated by the prospect of discovering a financial “Holy Grail.” However, designing and implementing a successful model poses many intellectual challenges. These include securing and cleaning data; acquiring a sufficient amount of financial domain knowledge; bounding the complexity...