Data Streams Models and Algorithms- P2

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. [...]... VIC3145,Australia Abstract 1 With the advance in both hardware and software technologies, automated data generation and storagehas become faster than ever Such data is referred to as data streams Streaming data is ubiquitous today and it is often a challenging task to store, analyze and visualize such rapid large volumes of data Most conventional data mining techniques have to be adapted to run in a streaming... whole data set or to transform the data vertically or horizontally to an approximate smaller size data representation Such an approach allows us to utilize many known data mining techniques to the case of data streams On the other hand, in task based solutions, some standard algorithmic modification techniques can be used to achieve time and space efficient solutions [13] Table 3.1 shows the data- based... Methods for Spatial Data Mining Very Large Data Bases Conference lease purchase PDF Split-Merge on www.verypdf.com to remove this watermark 38 DATA STREAMS: MODELS AND ALGORITHMS [23] O'Callaghan L., Mishra N., Meyerson A., Guha S., Motwani R (2002) Streaming -Data Algorithms For High-Quality Clustering ICDE Conference [24] Zhang T., Ramakrishnan R., and Limy M (1996) BIRCH: An Efficient Data Clustering... first data set series Bl OODlO indicates it contains lOOK points and 10 dimensions The same convention is used for the other data sets Figure 2.13 demonstrates that CluStream has linear scalability with the number of input clusters lease purchase PDF Split-Merge on www.verypdf.com to remove this watermark 36 7 DATA STREAMS: MODELS AND ALGORITHMS Discussion In this paper, we have discussed effective and. .. Dimensional Data for Data Mining Applications ACM SIGMOD Conference lease purchase PDF Split-Merge on www.verypdf.com to remove this watermark On Clustering Massive Data Streams: A Summarization Paradigm 37 [5] Aggarwal C (2003) A Framework for Diagnosing Changes in Evolving Data Streams ACM SIGMOD Conference [6] Aggarwal C., Han J., Wang J., Yu P (2003) A Framework for Clustering Evolving Data Streams. .. C, Han J., Wang J., Yu P (2004) On-Demand Classification of Evolving Data Streams ACM KDD Conference [8] Aggarwal C., Han J., Wang J., Yu P (2004) A Framework for Projected Clustering of High Dimensional Data Streams VLDB Conference [9] Aggarwal C (2006) on Futuristic Query Processing in Data Streams. EDBT Conference [lo] Ankerst M., Breunig M., Kriegel H.-P., Sander J (1999) OPTICS: Ordering Points... clustering data streams CluStream is implemented according to the description in this paper, and the STREAM K-means is done strictly according to [23], which shows better accuracy than BIRCH [24] To make the comparison fair, both CluStream and STREAM K-means use the same amount of memory lease purchase PDF Split-Merge on www.verypdf.com to remove this watermark 28 DATA STREAMS: MODELS AND ALGORITHMS... DATA STREAMS: MODELS AND ALGORITHMS 1 750 CluStream H STREAM 1250 1750 I 2250 Stream (in time units) Figure 2.4 Quality comparison (Network Intrusion dataset, horizon=256, stream-speed=200) Stream (in time units) Figure 2.5 Quality comparison (Charitable Donation dataset, horizon=4, streamspeed=200) lease purchase PDF Split-Merge on www.verypdf.com to remove this watermark On ClusteringMassive Data. .. bandwidth such as sensor networks and handheld devices Thus knowledge structure representation is an important issue After extracting models and patterns locally from data stream generators or receivers, it is important to transfer the data mining output to the user The user could be a mobile user or a stationary one getting the results from mobile nodes This is often a challenge because of the bandwidth... ClassificationMethods in Data Streams 43 static stored data sets However, this goal has not been fully realized for the case of data streams An important future research issue is to integrate the stream mining algorithms with known stream management systems in order to design complete systems for stream processing Hardware and other Technological Issues: The technological issue of mining data streams is an important