Data Streams: Models and Algorithms by Charu C. Aggarwal (auth.), Charu C. Aggarwal (eds.)

By Charu C. Aggarwal (auth.), Charu C. Aggarwal (eds.)

In fresh years, the development in expertise has made it attainable for agencies to shop and checklist huge streams of transactional information. Such information units which continually and swiftly develop through the years are often called information streams.

Data Streams: versions and Algorithms basically discusses concerns relating to the mining elements of information streams instead of the database administration element of streams. This quantity covers mining features of knowledge streams in a finished variety. every one contributed bankruptcy, from numerous popular researchers within the information mining box, includes a survey at the subject, the major rules within the box from that specific subject, and destiny study directions.

Data Streams: types and Algorithms is meant for a qualified viewers composed of researchers and practitioners in undefined. This e-book is usually acceptable for graduate-level scholars in machine science.

Charu C. Aggarwal got his B.Tech in computing device technological know-how from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a learn employees Member at IBM on account that then, and has released over ninety papers in significant meetings and journals within the database and knowledge mining box. He has utilized for, or been granted, over 50 US and foreign patents, and has two times been certain grasp Inventor at IBM for the industrial price of his patents. He has been granted 14 invention success awards via IBM for his patents. His paintings on actual time bio-terrorist possibility detection in facts streams received the IBM Epispire award for environmental excellence in 2003. He has served at the software committee of so much significant database meetings, and was once application chair for the information Mining and information Discovery Workshop, 2003, and a application vice-chair for the SIAM convention on information Mining, 2007. he's an affiliate editor of the IEEE Transactions on facts Engineering and an motion editor of the information Mining and data Discovery magazine. he's a senior member of the IEEE.

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Efficient and Effective Clustering Methods for Spatial Data Mining. Very Large Data Bases Conference. , Motwani R (2002). Streaming-Data Algorithms For High-Quality Clustering. ICDE Conference. , and Limy M (1996). BIRCH: An Efficient Data Clustering Method for Very Large Databases. ACM SIGMOD Conjkrence. Chapter 3 A SURVEY OF CLASSIFICATION METHODS IN DATA STREAMS Moharned Medhat Gaber, Arkady Zaslavsky and Shonali Krishnaswamy Caulfield School of Information Technology Monash University, 900 Dandenong Rd, Caulfield East, Melbourne VIC3145,Australia Abstract 1.

3. Clustering Evolving Data Streams: A Micro-clustering Approach The clustering problem is defined as follows: for a given set of data points, we wish to partition them into one or more groups of similar objects. The similarity of the objects with one another is typically defined with the use of some distance measure or objective function. The clustering problem has been 18 DATA STREAMS: MODELS AND ALGORITHMS widely researched in the database, data mining and statistics communities [I 2, 18,22,20,21,24] because of its use in a wide range of applications.

Clustering Data Streams. IEEE FOCS Conference. , Shim K. (1998). CURE: An Efficient Clustering Algorithm for Large Databases. ACM SIGMOD Conference. , Domingos P. (2001). Mining Time Changing Data Streams. ACMKDD Conference. , Dubes R. (1998). Algorithms for Clustering Data, Prentice Hall, New Jersey. , Rousseuw P. (1990). Finding Groups in Data- An Introduction to Cluster Analysis. MIey Series in Probability and Math. Sciences. , Han J (1994). Efficient and Effective Clustering Methods for Spatial Data Mining.

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