Hierarchical Clustering of Time Series Data Streams

1. Hierarchical Clustering of Time Series Data Streams Presenter : Shao-Wei Cheng Authors : Pedro Pereira Rodrigues, Joao Gama and Joao Pedro Pedroso ˜ ˜ TKDE 2007

2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Personal Comments

3. Hierarchical Clustering • Organized as a hierarchical tree. Data： 3

4. Time Series Data • Data streams usually consist of variables producing examples continuously over time. • The basic idea behind clustering streaming time series is to find groups of variables that behave similarly through time. • Each variable is a time series and each new example is the value of an observation of all time series in a particular moment. 4

5. Motivation • In recent real-world applications, data flows continuously from a data stream at high speed, producing examples over time. • Such as sensor data, web clicks, credit card usage, multimedia data, stock market. • Traditional models cannot adapt to the high speed arrival of new examples. So new algorithms have been developed that aim to process data in real-time. 5

6. Objectives • This paper presents and analyzes an incremental system for clustering streaming time series. • Online Divisive-Agglomerative Clustering (ODAC) • An adaptive system to perform hierarchical clustering of variables. • Memory usage • Time consumption 6

7. Methodology • Online Division-Agglomerative Clustering 7

8. Methodology Similarity and dissimilarity measure： Hoeffding bound： 8

9. Methodology 9

10. Experiments Detecting compact and well-separated clusters Independent of the number of clusters Hierarchy Quality 10

11. Experiments 11

12. Conclusion • Experimental results indicate that the performance is nearly equivalent to a batch divisive clustering on stationary time series. • They also reveal good performance on finding the correct number of clusters, obtained by a bunch of runs of k-means. • The main problem is how to achieve the trade-off between fast answer to changes and the time and space required to update the model.

13. Personal Comments • Advantage • The first proposal of a hierarchical approach to the problem. • Drawback • … • Application • Clustering of time series data streams.