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Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.

Artificial intelligence -- Congresses. --- Electronic data processing -- Distributed processing -- Congresses. --- Engineering & Applied Sciences --- Computer Science --- Data mining. --- Cluster analysis. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Computer science. --- Information technology. --- Business --- Database management. --- Statistics. --- Computer Science. --- Data Mining and Knowledge Discovery. --- Statistics for Business/Economics/Mathematical Finance/Insurance. --- IT in Business. --- Database Management. --- Data processing. --- Database searching --- Correlation (Statistics) --- Multivariate analysis --- Spatial analysis (Statistics) --- Statistics for Business, Management, Economics, Finance, Insurance. --- IT (Information technology) --- Technology --- Telematics --- Information superhighway --- Knowledge management --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Electronic data processing --- Statistics . --- Business—Data processing.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Nearly everyone knows K-means algorithm in the fields of data mining and business intelligence. But the ever-emerging data with extremely complicated characteristics bring new challenges to this "old" algorithm. This book addresses these challenges and makes novel contributions in establishing theoretical frameworks for K-means distances and K-means based consensus clustering, identifying the "dangerous" uniform effect and zero-value dilemma of K-means, adapting right measures for cluster validity, and integrating K-means with SVMs for rare class analysis. This book not only enriches the clustering and optimization theories, but also provides good guidance for the practical use of K-means, especially for important tasks such as network intrusion detection and credit fraud prediction. The thesis on which this book is based has won the "2010 National Excellent Doctoral Dissertation Award", the highest honor for not more than 100 PhD theses per year in China.

Mathematical statistics --- Business economics --- Information systems --- bedrijfseconomie --- data mining --- statistiek --- database management --- econometrie --- informatica management