Data Streams: Models and Algorithms by Charu C. Aggarwal

By Charu C. Aggarwal

This booklet basically discusses concerns concerning the mining facets of information streams and it really is certain in its basic concentrate on the topic. This quantity covers mining elements of information streams comprehensively: every one contributed bankruptcy features a survey at the subject, the major principles within the box for that exact subject, and destiny learn instructions. The ebook is meant for a qualified viewers composed of researchers and practitioners in undefined. This booklet is usually acceptable for advanced-level scholars in computing device technology.

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A typical example of such a technique is that of estimating the selectivityof queries. On Clustering Massive Data Streams: A Summarization Paradigm 27 In such cases, the summary statistics of micro-clusters can be used in order to estimate the number of data points which lie within a certain interval such as a range query. 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.

Yu P. (2004). A Framework for Projected Clustering of High Dimensional Data Streams. VLDB Conference. [9] Aggarwal C. EDBT Conference. , Sander J. (1999). ACMSIGMOD Conference. , Widom J. (2002). Models and Issues in Data Stream Systems, ACM PODS Conference. , Reina C. (1998) Scaling Clustering Algorithms to Large Databases. SIGKDD Conference. , Smith F. (2000). Hancock: A Language for Extracting Signatures from Data Streams. ACM SIGKDD Conference. , Hulten G . (2000). Mining High-speed Data Streams.

How do we decide the window or horizon of the training data to use so as to obtain the best classification accuracy? While techniques such as decision trees are useful for one-pass mining of data streams [14, 191, these cannot be easily used in the context of an on-demand classijier in an evolving environment. This is because such a classifier requires rapid variation in the horizon selection process due to data stream evolution. Furthermore, it is too expensive to keep track of the entire history of the data in its original fine granularity.

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