Mining high-dimensional data, sequences and data streams / Prospection de données de grande dimension, de séquences et de flux de donées

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In recent years, the size of databases has increased rapidly and data sources capable of continuously producing large flows of data are becoming increasingly common. For instance, the Yahoo home page receives 166 million hits per day and about 48 Gb of clickstream collection per hour. Other examples include traffic and weather data, satellite observations, banking transactions and genomic sequences via high-throughput technologies. There is an increasing need for efficient data mining methods and knowledge discovery systems to utilize this data. Cluster analysis, which is aimed at separating data into meaningful groups, is a major sub-domain of data mining. Clustering high-dimensional data, sequence data and data streams is very difficult because conventional measures of the distance/similarity between data objects are not meaningful, the structure in these data sets is complex and real time processing is often required. These difficulties are further amplified if the data type is categorical rather than numerical.