Data Streams: Models and Algorithms by Charu C. Aggarwal

By Charu C. Aggarwal

This ebook basically discusses matters concerning the mining features of knowledge streams and it truly is designated in its fundamental specialize in the topic. This quantity covers mining facets of information streams comprehensively: every one contributed bankruptcy encompasses a survey at the subject, the major rules within the box for that specific subject, and destiny examine instructions. The e-book is meant for a certified viewers composed of researchers and practitioners in undefined. This e-book can also be acceptable for advanced-level scholars in laptop technological know-how.

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Such a requirement reduces the number of micro-clusters that can be stored by the available memory and therefore reduces the effectiveness of the algorithm. We will find a way to approximate the average timestamp of the last m data points of the cluster M. This will be achieved by using the data about the timestamps stored in the micro-cluster M. We note that the timestamp data allows uito calculate the mean and standard deviation3 of the arrival times of points in a given micro-cluster M. Let these values be denoted by pM and OM respectively.

The KDD-CUP'98 Charitable Donation data set has also been used in evaluating several one-scan clustering algorithms, such as [16]. This data set contains 95412 records of information about people who have made charitable donations in response to direct mailing requests, and clustering can be used to group donors showing similar donation behavior. As in [16], we will only use 56 fields which can be extracted from the total 481 fields of each record. This data set is converted into a data stream by taking the data input order as the order of streaming and assuming that they flow-in with a uniform speed.

To make the comparison fair, both CluStream and STREAM K-means use the same amount of memory. 28 DATA STREAMS: MODELS AND ALGORITHMS Specifically, they use the same stream incoming speed, the same amount of memory to store intermediate clusters (called Micro-clusters in CluStream), and the same amount of memory to store the final clusters (called Macro-clusters in CluStream). Because the synthetic datasets can be generated by controlling the number of data points, the dimensionality, and the number of clusters, with different distribution or evolution characteristics, they are used to evaluate the scalability in our experiments.

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