By Charu C. Aggarwal
This booklet essentially discusses concerns with regards to the mining features of knowledge streams and it truly is exact in its fundamental specialise in the topic. This quantity covers mining points of information streams comprehensively: each one contributed bankruptcy encompasses a survey at the subject, the foremost principles within the box for that individual subject, and destiny examine instructions. The ebook is meant for a certified viewers composed of researchers and practitioners in undefined. This booklet can also be acceptable for advanced-level scholars in desktop technological know-how.
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Extra resources for Data Streams: Models and Algorithms
The KDD-CUP'98 Charitable Donation data set has also been used in evaluating several one-scan clustering algorithms, such as . 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 , 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.
One interesting characteristic of the geometric time window is that for any userspecified time window of h, at least one stored snapshot can be found within a factor of 2 of the specified horizon. This ensures that sufficient granularity is available for analyzing the behavior of the data stream over different time horizons. We will formalize this result in the lemma below. 4 Let h be a user-specijied time window, and t, be the current time. Let us also assume that max-capacity such that h/2 5 t, - t, I:2 .
This is quite a modest requirement given the fact that a snapshot within a factor of 2 can always be found within any user specified time window. It is possible to improve the accuracy of time horizon approximation at a modest additional cost. 1. An example of snapshots stored for a = 2 and 1 = 2 of order r for 1 > 1. In this case, the storage requirement of the technique corresponds to (az 1) log, (T) snapshots. On the other hand, the accuracy of time horizon approximation also increases substantially.