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A Framework for Projected Clustering of High-Dimensional Data Streams

This paper presents a novel framework for clustering high-dimensional data streams, focusing on the innovative concept of fading cluster structures. It emphasizes that not all dimensions should be utilized for clustering and introduces the HPStream algorithm to efficiently manage and maintain clusters over time. By employing a combination of k-means for initial clustering and a novel projection-based distance metric, the method allows for improved cluster management in evolving datasets. Comparative experiments on synthetic and real-world data demonstrate the accuracy, efficiency, sensitivity, and scalability of HPStream against existing algorithms.

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A Framework for Projected Clustering of High-Dimensional Data Streams

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  1. A Framework for Projected Clustering of High Dimensional Data Streams Proceedings of the 30th VLDB Conference, Toronto, Canada, 2004

  2. Motivation and Underlying Concepts • All dimensions should not be considered in high dimensional setup for clustering • The Fading Cluster Structure: Use fading function • The half life t0 of a point is defined as the time at which f(t0) = (1=2)f(0). • A fading cluster structure at time t for a set of d-dimensional points • The clustering structure properties called additivity and temporal multiplicity • The clustering process requires a simultaneous maintenance of the clusters as well as the set of dimensions associated with each cluster

  3. HPStream : High-Dimentional Projected Stream Clustering Method

  4. HPStream Algorithm – Brief Explanation -Set parameters -Normalization Process -Initial Clustering using k-means and Init Number -ComputeDimensions: This procedure determines the dimensions in such a way that the spread along the chosen dimensions is as small as possible -The next step is the determination of the closest cluster to the incoming data point using FindProjectedDist -The procedure for determination of the limiting radius is denoted by FindLimitingRadius -Finally decision which cluster to add or delete.

  5. Experimental Setup HPStream compared with Clustream : both implemented on MS VC++ One synthetic data and 2 sets of Real world data - Network Intrusion and Forest cover type data sets. Comparison criteria for judging the 2 algorithms: - accuracy : clustering quality - efficiency : stream processing rate - sensitivity : varying decay rate, l and radius threshold - scalability : varying number of dimensions and clusters Parameters initialized as following: Decay-rate = 0:5, Spread radius factor = 2, InitNumber =2000, Average Projected Dimensionality l > d/2.

  6. Comparing Accuracy : Using clustering quality and cluster purity

  7. Accuracy comparison continued:

  8. Accuracy comparison continued:

  9. Efficiency comparison using Stream Processing Rate:

  10. Sensitivity : Varying ‘l’

  11. Sensitivity: Varying radius threshold and decay rate

  12. Scalability : varying dimensionality and number of clusters

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