Change detection in data streams through unsupervised learning

In many cases, databases are in constant evolution, new data is arriving continuously. Data streams pose several unique problems that make obsolete the applications of standard data analysis methods. Indeed, these databases are constantly on-line, growing with the arrival of new data. In addition, the probability distribution associated with the data may change over time. We propose in this paper a method of synthetic representation of the data structure for efficient storage of information, and a measure of dissimilarity between these representations for the detection of change in the stream structure.