The goal of this book is to help readers understand state-of-the-art techniques in biological data mining and data management and includes topics such as:

- preprocessing tasks such as data cleaning and data integration as applied to biological data

- classification and clustering techniques for microarrays

- comparison of RNA structures based on string properties and energetics

- discovery of the sequence characteristics of different parts of the genome

- mining of haplotypes to find disease markers

- sequencing of events leading to the folding of a protein

- inference of the subcellular location of protein activity

- classification of chemical compounds based on structure

- special purpose metrics and index structures for phylogenetic applications

- a new query language for protein searching based on the shape of proteins

- very fast indexing schemes for sequences and pathways

Aimed at computer scientists, necessary biology is explained.

Written especially for computer scientists, all necessary biology is explained.

Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.

The14thPaci?c-AsiaConferenceonKnowledgeDiscoveryandData Mining was held in Hyderabad, India during June 21-24, 2010; this was the ?rst time the conference was held in India. PAKDDisamajorinternationalconferenceintheareasofdatamining (DM) and knowledge discovery in databases (KDD). It provides an international - rum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and automatic scienti?c discovery, data visu- ization, causal induction and knowledge-based systems. PAKDD-2010 received 412 research papers from over 34 countries incl- ing: Australia,Austria,Belgium, Canada,China, Cuba, Egypt,Finland, France, Germany, Greece, Hong Kong, India, Iran, Italy, Japan, S. Korea, Malaysia, Mexico,TheNetherlands,NewCaledonia,NewZealand,SanMarino,Singapore, Slovenia,Spain, Switzerland, Taiwan, Thailand, Tunisia, Turkey, UK, USA, and Vietnam. This clearly re?ects the truly international stature of the PAKDD conference. AfteraninitialscreeningofthepapersbytheProgramCommitteeChairs,for papers that did not conform to the submission guidelines or that were deemed not worthy of further reviews, 60 papers were rejected with a brief expla- tion for the decision. The remaining 352 papers were rigorously reviewed by at least three reviewers. The initial results were discussed among the reviewers and ?nally judged by the Program Committee Chairs. In some cases of c- ?ict additional reviews were sought. As a result of the deliberation process, only 42 papers (10.2%) were accepted as long presentations (25 mins), and an ad- tional 55 papers (13.3%) were accepted as short presentations (15 mins). The total acceptance rate was thus about 23.5% across both categories.

This book constitutes the refereed proceedings of the First International Workshop on Knowledge Discovery from XML Documents, KDXD 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The ten revised full papers presented together with two invited talks were carefully reviewed and selected from 26 submissions. The papers are organized in topical sections.

With the unprecedented growth-rate at which data is being collected and stored electronically today in almost all fields of human endeavor, the efficient extraction of useful information from the data available is becoming an increasing scientific challenge and a massive economic need. This book presents thoroughly reviewed and revised full versions of papers presented at a workshop on the topic held during KDD'99 in San Diego, California, USA in August 1999 complemented by several invited chapters and a detailed introductory survey in order to provide complete coverage of the relevant issues. The contributions presented cover all major tasks in data mining including parallel and distributed mining frameworks, associations, sequences, clustering, and classification. All in all, the volume presents the state of the art in the young and dynamic field of parallel and distributed data mining methods. It will be a valuable source of reference for researchers and professionals.

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

With the unprecedented rate at which data is being collected today in almost all fields of human endeavor, there is an emerging economic and scientific need to extract useful information from it. Data mining is the process of automatic discovery of patterns, changes, associations, and anomalies in massive databases. This book will provide an introductory survey of the main topics in data mining and knowledge discovery, including: classification, clustering, association rules, sequence similarity, and so on. Emphasis will be laid on performance and implementation issues, as well as on applications such as web mining.