Incremental Clustering in Data Mining Using Genetic Algorithm
Data warehouses provide a great deal of opportunities for performing data mining tasks such as classification and clustering. Typically, updates are collected and applied to the data warehouse periodically. Then, all patterns derived from the warehouse by some data mining algorithm have to be updated as well. Due to the very large size of the databases, it is highly desirable to perform these updates incrementally. In this paper, the authors present the new approach/algorithm based on genetic algorithm. Their algorithm is applicable to any database containing data from a metric space, e.g., to a spatial database.