By Charu C. Aggarwal (auth.), Charu C. Aggarwal (eds.)
In contemporary years, the growth in expertise has made it attainable for businesses to shop and checklist huge streams of transactional facts. Such information units which regularly and speedily develop over the years are often called facts streams.
Data Streams: types and Algorithms essentially discusses concerns regarding the mining points of knowledge streams instead of the database administration element of streams. This quantity covers mining facets of information streams in a entire kind. every one contributed bankruptcy, from various popular researchers within the information mining box, encompasses a survey at the subject, the major principles within the box from that specific subject, and destiny study directions.
Data Streams: versions and Algorithms is meant for a qualified viewers composed of researchers and practitioners in undefined. This e-book is additionally applicable for graduate-level scholars in desktop science.
Charu C. Aggarwal bought his B.Tech in desktop technology from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a examine employees Member at IBM considering 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 overseas patents, and has two times been special grasp Inventor at IBM for the economic worth of his patents. He has been granted 14 invention success awards via IBM for his patents. His paintings on actual time bio-terrorist hazard detection in facts streams gained the IBM Epispire award for environmental excellence in 2003. He has served at the application committee of so much significant database meetings, and used to be software chair for the knowledge Mining and information Discovery Workshop, 2003, and a application vice-chair for the SIAM convention on info Mining, 2007. he's an affiliate editor of the IEEE Transactions on info Engineering and an motion editor of the knowledge Mining and information Discovery magazine. he's a senior member of the IEEE.
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Additional info for Data Streams: Models and Algorithms
Such an approach can be very efficient in a variety of applications since voluminous data streams are difficult to use if they need to be utilized for query estimation. However, the microclustering approach can condense the data into summary statistics, so that it is possible to efficiently use it for various kinds of queries. We note that the technique is quite flexible as long as it can be used for different kinds of queries. An example of such a technique is illustrated in , in which we use the micro-clustering technique (with some modifications on the tracked statistics) for futuristic query processing in data streams.
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. Recently, the clustering problem has also been studied in the context of the data stream environment [17,23]. A previous algorithm called STREAM  assumes that the clusters are to be computed over the entire data stream.
12 shows the experimentalresults, from which one can see that CluStream has linear scalability with data dimensionality. For example, for dataset series B400C20, when the dimensionality increases from 10 to 80, the running time increases less than 8 times from 55 seconds to 396 seconds. Another three series of datasets were generated to test the scalability against the number of clusters by varying the number of input clusters from 5 to 40, while fixing the stream size and dimensionality. For example, the first data set series Bl OODlO indicates it contains lOOK points and 10 dimensions.