A New Trend of Knowledge Discovery Toward Intelligent Data Analysis. A New Trend of KDD Using DM and FCARB

      Аннотация: The objectives of this book are studying, analyzing and suggesting a solution for the main problems (challenges) in the knowledge discovery in database field, the problems are included missing values, data scarcity, looking for data mining algorithms as a black box, reduction the three main dimensions and design mathematical model of KDD systems (i.e., Design Meta Knowledge System). This book concludes the strong relationship between KDD and IDA where each one completes the other. In general, we can summarize the main benefits points of suggest system "NTKDD" as followings: 1. Suggest the DRFLLS as a tool to handle One of the still problems in estimation missing value method is how to select the optimal number of nearest neighbors of the missing values. 2. Handel one of the main problems of machine learning and data mining called"Data scarcity problem By building new Technique GPDCM. 3. Treating the problem of Reduction the Three Main Dimensions by using the structure marriage between the benefit of PCA and new suggest a technique called FP-KC. 4. Can converting the black box data mining algorithm to white box though Design Mathematical models of knowledge resulting from

Ключевые слова: Data Mining, Intelligent data analysis, Developed Random Forest and Local Least Square (DRFLLS), Genetic Programming Data Construction Method (GPDCM), Frequency Pattern-Knowledge Construction (FP-KC), Miner of obtaining accurate and comprehensible classified association rules (MOACCR), Develop Fuzzy Classified Association Rule Base (DFCARB) and mathematical models.