C. C. Aggarwal. A framework for diagnosing changes in evolving data streams. In SIGMOD, 2003. Home/Search Document Details and Download
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A Framework for Projected Clustering of High - Dimensional Data Streams Self-citation (Aggarwal) (Correct)
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C. C. Aggarwal. A Framework for Diagnosing Changes in Evolving Data Streams. ACM SIGMOD Conference, pp. 575--586, 2003.
On Demand Classification of Data Streams - Charu Aggarwal Charu Self-citation (Aggarwal) (Correct)
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C. C. Aggarwal. A Framework for Diagnosing Changes in Evolving Data Streams. ACM SIGMOD Conference, 2003.
A Framework for Projected Clustering of High - Dimensional Data Streams (2004) Self-citation (Aggarwal) (Correct)
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C. C. Aggarwal. A Framework for Diagnosing Changes in Evolving Data Streams. ACM SIGMOD Conference, pp. 575--586, 2003.
On Demand Classification of Data Streams - Charu Aggarwal Charu Self-citation (Aggarwal) (Correct)
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C. C. Aggarwal. A Framework for Diagnosing Changes in Evolving Data Streams. ACM SIGMOD Conference, 2003.
A Framework for Projected Clustering of High Dimensional.. - Aggarwal, Han, Wang, Yu (2004) Self-citation (Aggarwal) (Correct)
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C. C. Aggarwal. A Framework for Diagnosing Changes in Evolving Data Streams. ACM SIGMOD Conference, pp. 575--586, 2003.
A Framework for Clustering Evolving Data Streams - Aggarwal, Han, Wang, Yu (2003) (11 citations) Self-citation (Aggarwal) (Correct)
....of everyday life at a rapid rate. Such processes lead to large amounts of data which grow at an unlimited rate. These data processes are referred to as data streams. The data stream problem has been extensively researched in recent years because of the large number of relevant applications [1, 3, 6, 8, 13]. In this paper, we will study the clustering problem for data stream applications. The clustering problem is defined as follows: for a given set of data points, we wish to partition them into one or more groups of similar objects. The similarity of the objects with one another is typically ....
....user considerable flexibilities for querying the stored micro clusters with di#erent levels of granularity and time horizons. 5 Evolution Analysis of Clusters Many interesting changes can be recorded by an analyst in an evolving data stream for e#ective use in a number of business applications [1]. In the context of the clustering problem, such evolution analysis also has significant importance. For example, an analyst may wish to know how the clusters have changed over the last quarter, the last year, the last decade and so on. For this purpose, the user needs to input a few parameters to ....
C. C. Aggarwal. A Framework for Diagnosing Changes in Evolving Data Streams. ACM SIGMOD Conference, 2003.
Streaming Pattern Discovery in Multiple Time-Series - Papadimitriou, Sun, Faloutsos (2005) (Correct)
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C. C. Aggarwal. A framework for diagnosing changes in evolving data streams. In SIGMOD, 2003.
Mining Data Streams - Jin (Correct)
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C.C. Aggarwal. A framework for diagnosing changes in evolving data streams. In ACM SIGMOD, 2003.
Thesis Proposal - Ruoming Jin Department (Correct)
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C.C. Aggarwal. A framework for diagnosing changes in evolving data streams. In ACM SIGMOD, 2003.
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