Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. Please purchase PDF Split-Merge on www.verypdf.com to remove this watermark. [...]... 102 DATA STREAMS: MODELS AND ALGORITHMS [16] Hulten G., Spencer L., Domingos P (2001) Mining Time Changing Data Streams ACM KDD Conference [17] Jain A., Dubes R (1998) Algorithms for Clustering Data, Prentice Hall, New Jersey [18] Kifer D., David S.-B., Gehrke J (2004) Detecting Change in Data Streams VLDB Conference,2004 [19] Roddick J F et a1 (2000) Evolution and Change in Data Management: Issues and. .. Community Evolution in Data Streams ACM SIAM Conference on Data Mining [3] Aggarwal C (2003) A Framework for Diagnosing Changes in Evolving Data Streams ACM SIGMOD Conference [4] Aggarwal C (2002) An Intuitive Framework for understanding Changes in Evolving Data Streams IEEE ICDE Conference [5] Aggarwal C., Han J., Wang J., Yu P (2003) A Framework for Clustering Evolving Data Streams VLDB Conference... different from and orthogonal to the work in [13, 141 Specifically, the work in [13,14] is focussed on the effects of evolution on data mining models and algorithms While these results show some interesting results in terms of generalizing existing data mining algorithms, our view is that data streams have special mining requirements which cannot be satisfied by using existing data mining models and algorithms... dynamically, and changing rapidly Due to limited resources available and the usual requirements of fast response, most data streams may not be fully stored and may only be examined in a single pass These characteristics of stream data have been emphasized and explored in their investigationsby many researchers, such as ([6, 8, 17, 18, 16]), and efficient stream data querying, counting, clustering and classification... which the data is being reduced, those at which the data is increasing, and those from where the data is shifting to other locations: DEFINITION A data coagulationfor timeslice t and user defined threshold 5.1 min-coag is defined to be a connected region R in the data space, so that for eachpoint X E R, we have yh,,ht) t ) > min-coag > 0 (X, Thus, a data coagulation is a connected region in the data which... stream data Three importanttechniques are proposed for lease purchase PDF Split-Merge on www.verypdf.com to remove this watermark DATA STREAMS: MODELS AND ALGORITHMS the design and implementation of stream cubes First, a tilted timeframe model is proposed to register time-related data in a multi-resolution model: The more recent data are registered at finer resolution, whereas the more distant data. .. reversed and the data stream arrived in reverse order, starting at t ht and ending at t Examples of the forward and reverse density profiles are illustrated in Figures 5.1 and 5.2 respectively For a given spatial location X and time T , let us examine the nature of the functions F(h,,ht)( X ,T ) and R(h,,ht)( X ,T - ht) Note that both functions are almost exactly the same, and use the same data points... conjunction with data mining algorithms In this section, we will discuss the effects of evolution on data mining algorithms The problem of mining incremental data dynamically has often been studied in many data mining scenarios [7, 10, 12, 241 However, many of these methods are often lease purchase PDF Split-Merge on www.verypdf.com to remove this watermark 98 DATA STREAMS: MODELS AND ALGORITHMS not... amount of data in order to provide effective results, and historical clusters do provide good insights about the future clusters in the data Therefore, it makes more sense to use all the data, but with an application specific decaybased approach which provides the new data greater weight than the older data On the other hand, in problems such as classification, the advantages of using more data is much... estimation and its application to different kinds of visual representations of changes in the underlying data We also discussed the problem of online community evolution in fast data streams In many of these methods, clustering is a key component since it allows us to summarize the data effectively We also studied the reverse problem of how data mining models are maintained when the underlying data changes