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Hierarchical Time-Series Clustering for Data Streams: Overview and ODAC Algorithm Steps

This presentation outlines the hierarchical time-series clustering approach for data streams, focusing on the ODAC algorithm. Key steps include obtaining next examples, updating sufficient statistics, computing the Hoeffding bound, and ordering clusters by diameter. The process identifies potential split points and aggregation opportunities within clusters, iterating until all data has been processed. By systematically testing clusters and applying hierarchical methods, we enhance our ability to analyze temporal data efficiently.

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Hierarchical Time-Series Clustering for Data Streams: Overview and ODAC Algorithm Steps

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  1. Hierarchical Time-Series Clustering for Data Streamsby Pedro Rodrigues et al.KDD’04 Piyush Ranjan Satapathy A class Presentation for CS235: Data Mining Techniques

  2. ODAC Algorithm 1. Get nmin next examples 2. Update and propagate sufficient statistics for all variables 3. Compute the Hoeffding bound 4. Choose next cluster Ck in descending order of diameters 5. TestSplit() in cluster Ck 6. If we found a split point, goto 12. with new cluster tree 7. If still exists a cluster Ck not yet tested for splitting goto 4. 8. Choose next cluster Ck in ascending order of diameters 9. TestAggregate() in cluster Ck 10. If we found an aggregation then goto 12. with new cluster tree 11. If still exists a cluster Ck not yet tested for aggregation goto 8. 12. If not end of data, goto 1.

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