Efficient Data Mining for Frequent Itemsets in Dynamic and Distributed Databases

Data Mining is one of the central activities associated with understanding and exploiting the world of digital data. It is the mechanized process of modeling large databases by means of discovering useful patterns. A frequent itemset is a pattern describing a relevant subset of the data, and a collection of frequent itemsets is particularly useful because it is an extremely compact model of the database. Discovering frequent itemsets in large databases is usually a hard computational task, which can be even harder when data is dynamic and distributed. Applying traditional algorithms in such data results in high communication overhead, excessive wastage of CPU and I/O resources, privacy violations, and often does not meet the stringent rapid response times, to essentially an interactive process of exploiting the data. Hence, there is an urgent need for non-trivial algorithms that can effectively mine frequent itemsets in dynamic and distributed databases. Such algorithms are presented in this master thesis