Clustering Data Streams

1
Clustering Data Streams

• Chun Wei
• Dept Computer Information Technology
• Advisor Dr. Sprague

2
Data Stream

• Massive data sets accumulated at an astonishing
  rate.
• Examples
• Tracking network data to study change in traffic
  patterns and possible intrusions
• Tracking meteorological data, such as temperatures

3
NASA MISR satellite

• Collects several TB of satellite imagery data
  daily

4
Challenges

• Compactness of data representation
• Fast, incremental processing of new data points
  (one-pass and linear access of data)
• Clear and fast identification of changes in
  evolving clustering models

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Compactness

• Utilize a data structure that summarizes a group
  of data points, minimizing the storage space
• The space required does not grow appreciably with
  the number of points processed

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Incremental Processing of data

• When clustering new data points, the algorithm
  should not require comparison with all the points
  processed in the past
• The data must be processed as they are produced.
  Linear scan is required, random access is
  prohibitively expensive.

7
Identification of Changes

• The algorithm must be able to
• diagnose changes in evolving data streams
• distinguish outliers from data points that
  represent a new cluster

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Current Algorithms

• BIRCH
• STREAM
• CLU-STREAM

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BIRCH

• Use CF vectors to store data
• CF (N, ?Xi2 , ? Xi ) Xi is a vector
• Store the number of points, the linear sum and
  the square sum of all data points in a
  micro-cluster
• Sufficient to calculate centroids, radius,
  diameter and distances

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B-Tree
Root
29
16
22
7
39
3
5
19
20
22
24
27
38
2
7
14
16
29
33
34
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Building of CF Tree

• B-Tree, with a branch factor B, threshold T and L
  maximum number of entries in a leave node

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Adjusting CF Tree

• Increases the threshold T so that each leaf entry
  to absorb more points. T can be set as radius or
  diameter.
• Leaf entries with far fewer points are regarded
  as outliers and written back to disk.

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STREAM

• Process data streams in batches of points
• Use weighted centroids Ci to represent ith batch
  of points.
• Recursively cluster the weighted centroids until
  k-clusters

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Problems with BIRCH STREAM

• Old data points are equally important as new data
  points
• May not be able to detect new trends in evolving
  data stream

15
CLU-STREAM

• Also use CF vectors to store data summary
• Use time stamps to record the elapsed time from
  the beginning
• Take snapshots at different time stamp, favoring
  the most recent data
• (Snapshot micro-clusters stored at particular
  moments in the stream)

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CLU-STREAM (continue)

• A snapshot contains q micro-clusters, q depends
  on the memory available
• New data points will be assigned to one of the
  micro-clusters in previous snapshot if it falls
  within the maximum boundary of that
  micro-cluster.

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CLU-STREAM (continue)

• If a new data points fails to fit into any
  current cluster, a new cluster is created, and an
  existing one is deleted or two merged.
• A cluster is removed if the average time-stamp
  when it absorbs m new data points is least
  recent.

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Detect New Trends

• Comparing clustering results from snapshots to
  snapshots reveals trends in evolving data stream.

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References

• Aggarwal, C. C., Han J., Wang, J. Yu, P. S.
  (2003). A Framework for Clustering Evolving Data
  Stream. In Proc. of the 29th VLDB Conference.
• Barbara, D. (2003). Requirements for Clustering
  Data Streams. SIGKDD Explorations, 3 (2), 23-27.
• Ester M., Kriegel H.-P., Sander J. Xu X (1998).
  Clustering for Mining in Large Spatial Databases.
  Special Issue on Data Mining, KI-Journal,
  ScienTec Publishing, No. 1.
• Guha, S., Meyerson, A., Mishra, N. Motwani, R.,
  Callaghan, L. (2003). Clustering Data Streams
  Theory and Practice. IEEE Transactions on
  Knowledge and Data Engineering, 15 (3), 515-528.
• Zhang, T., Ramakrishnan R. Livny, M. (1996).
  BIRCH An Efficient Data Clustering Method for
  Very Large Databases. In Proc. of ACM SIGMOD
  International Conference on Management of Data.