By Charu C. Aggarwal (auth.), Charu C. Aggarwal (eds.)
In contemporary years, the development in expertise has made it attainable for agencies to shop and checklist huge streams of transactional info. Such info units which regularly and speedily develop over the years are often called facts streams.
Data Streams: versions and Algorithms essentially discusses concerns with regards to the mining facets of information streams instead of the database administration point of streams. This quantity covers mining features of knowledge streams in a finished type. every one contributed bankruptcy, from various popular researchers within the facts mining box, includes a survey at the subject, the most important principles within the box from that specific subject, and destiny examine directions.
Data Streams: types and Algorithms is meant for a qualified viewers composed of researchers and practitioners in undefined. This e-book can be applicable for graduate-level scholars in computing device 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 considering the fact 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 unique grasp Inventor at IBM for the industrial price of his patents. He has been granted 14 invention success awards by way of IBM for his patents. His paintings on genuine time bio-terrorist chance detection in facts streams gained the IBM Epispire award for environmental excellence in 2003. He has served at the software committee of so much significant database meetings, and used to be application chair for the information Mining and data 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 info Engineering and an motion editor of the information Mining and information Discovery magazine. he's a senior member of the IEEE.
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Extra resources for Data Streams: Models and Algorithms
Varying Horizons for the classification process Classification of Data Streams: A Micro-clustering Approach One important data mining problem which has been studied in the context of data streams is that of stream classification . The main thrust on data stream mining in the context of classificationhas been that of one-pass mining [14,19]. In general, the use of one-pass mining does not recognize the changes which have occurred in the model since the beginning of the stream construction process .
The nature of the clusters may vary with both the moment at which they are computed as well as the time horizon over which they are measured. For example, a data analyst may wish to examine clusters occurring in the last month, last year, or last decade. Such clusters may be considerably different. Therefore, we assume that one of the inputs to the clustering algorithm is a time horizon over which the clusters are found. Next, we will discuss CluStream, the online algorithm used for clustering data streams.
In this case, any time horizon can be approximated to a factor of (1 + l/az-l). 3 Let h be a user specijied time horizon, t, be the current time, and t, be the time of the last stored snapshot of any orderjust before the time t, - h. Then t, - t, < (1 + l/az-l) - h. Proof: Similar to previous case. For larger values of I , the time horizon can be approximated as closely as desired. 2%, while a total of only (2'' 24 * 60 * 60) = 32343 snapshots are required for 100 years. Since historical snapshots can be stored on disk and only the current snapshot needs to be maintained in main memory, this requirement is quite feasible from a practical point of view.