Tracking Clusters in Evolving Data Sets

As organizations accumulate data over time, the problem of tracking how patterns evolve becomes important. In this paper, we present an algorithm to track the evolution of cluster models in a stream of data. Our algorithm is based on the application of bounds derived using Cherno 's inequality and makes use of a clustering algorithm that was previously developed by us, namely Fractal Clustering, which uses self-similarity as the property to group points together. Experiments show that our tracking algorithm is e cient and e ective in nding changes on the patterns.

• Type: Conference
• Year: 2001
• Where: FLAIRS
• Authors: Daniel Barbará, Ping Chen
• Keywords: Algorithm, Artificial Intelligence, Clustering Algorithm, FLAIRS 2001, Organizations Accumulate Data