Knowledge Engineering

Knowledge engineering (KE) and data mining are areas of common interest to researchers in AI, pattern recognition, statistics, databases, knowledge acquisition, data visualization, high performance computing, and expert systems.

This book is divided in to seven major parts. Part one has focused on document and multi-document reconstruction and summarization, Medical Imaging, Opinion Mining, PCA & LDA, Cross co-relation and phase based matching. Whereas part two covers application areas of Data Mining like Data Cleaning, Weather forecasting and Web Mining. Part three covers HCI, ECG, Direct Manipulation Interface, Face Recognition in crowd, Gesture recognition for Mobile, Chaotic dynamics, epilepsy and Alzheimer's diagnosis, CAL, Devanagri character recognition and Speech Databases. Web Mining related areas like Clustering, Web usage Mining, Web log analysis, BI, Web indexing, Crawlers and Link Mining are covered in part four.

The algorithms of Data Mining related to Decision Trees, Association Rules and Tries base Apriori algorithm, Decision support and GIS are covered in part five. The sixth part covers aspects of Security like density based approach, intrusion detection in Oracle, unbalanced datasets and dark block extraction. The last part contains the other allied areas of Data Mining for the applications like customer review, SOA-Governance & planning, Mobile Ad-Hoc networks, KE Framework for technical education institutes, time series analysis, extraction of genetic features, KD in Agriculture crop production, Earthquake prediction and Credit Card fraud detection.