Advances In Data Mining Knowledge

Data mining can be de ned as a process of exploring and analysis for large amounts of data with a speci c target on discovering signi cantly important patterns and rules. Data mining helps nding knowledge from raw, unprocessed data. Using data mining techniques allows extracting knowledge from the data mart, data warehouse and, in particular cases, even from operational databases. In this context, data mining gets an important role in helping organizations to understand their customers and their behavior, keeping clients, stocks anticipation, sale policies optimization as well as other bene ts which bring a considerable competitive advantage to the organization. Advances in Data Mining Knowledge presents knowledge discovery and data mining applications. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, arti cial intelligence, and other areas. The objective of rst chapter is to investigate the impact of various data representations on predictive data mining models. Second chapter focuses on inconsistent decision system. Third chapter discusses techniques for processing large-scale data. Fourth chapter aims to evaluate di erent algorithms for classi cation of modulation signals on spectrum sensing. Fifth chapter reveals on electric load forecasting using data mining techniques. Electric load forecasting can be divided into three categories that are short term load forecasting, medium term load forecasting and long term load forecasting. Sixth chapter explores on data mining from remote sensing snow and vegetation product. Seventh chapter highlights on how to improve decision support systems with data mining techniques and eighth chapter focuses on bot net detection. Botnets are as powerful as they can di use on new hosts by infecting them via well-known security holes. One can be infected by clicking a link on a website, opening an attachment of an e-mail or only viewing them, sur ng on a websi