Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining and analytics. Users will find the authors’ research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data. The book's authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. In addition, a number of descriptive learning tasks are included.

This book originally grew out of our research in the field of spatial data analysis using clustering techniques. Data analysis becomes quite challenging when several variables are involved. This book deals with various types of clustering techniques and their applications, specially in the field of spatial data analysis in order to find interesting patterns. We have started our book by an introduction to the field of data mining and given special emphasis on cluster analysis which is a task in data mining. Our aim is to impart the knowledge necessary for making proper interpretations, selecting appropriate techniques, understanding their strengths and weaknesses and making it understandable to the readers.

This monograph presents a study on data mining techniques and its application in medical image analysis for cancer detection. An extensive survey and comparative study on various clustering and association mining techniques has been presented. Monograph also reported one new density based clustering technique EnDBSCAN that can detect embedded cluster structure and OPAM- an efficient one pass technique for mining frequent item sets. Results have been reported to establish the effectiveness of the works in light of various synthetic datasets. Finally it discusses about medical imaging and potentials of data mining techniques for analyzing medical imagery for detecting disease like cancer. As a case study it discusses two cancer type namely brain and skin cancer. It also proposes architecture for automatic brain cancer detection tool based on Data Mining techniques.

Reflects current research in natural language processing, data mining, networks and network security, robotics, image processing and bioinformatics. Problems such as named entity recognition, creation of WordNet and part-of-speech tagging have been addressed in Section I. In Section II, video denoising and compression have been discussed. Section III includes surveys on view selection, subspace clustering and density based spatial data clustering, besides work on forecasting technique and seed selection for C-means and k-means clustering algorithms. Similarly, surveys on MAC protocols for Cognitive Radio, QoS in wireless networks, and mobile agents in application migration are presented in Section IV. This section also contains survey on attacks on spanning tree protocol, VLAN attacks, state based modelling of intrusion detection, and privacy preserving data mining, as well as an authentication mechanism using fingerprint classification. Section V contains an extension of open source iCub simulator, some interesting results on grasp type recognition, and a discussion on the significance of metacognition in building an intelligent system. Finally, Section VI covers a survey on subspace clustering in gene expression data, and a distributed clustering technique for large gene expression data.