A Self-Adaptive Insert Strategy for Content-Based Multidimensional Database Storage

In this paper, we present the current development progress of our dynamic insert strategy based on the Intelligent Cluster Index (ICIx), which is a new type of multidimensional database storage. Opposite to purely value-based interval methods, ICIx performs a semantic clustering of the data objects in a database and keeps the clustering results as basis for storing in a special tree structure (V-Tree). Our paper aims at the quality problem caused by a trade-off between the static clustering that results from the initial training data set and the continuous insertion of data into a database which requires a continuous classification. The strategy that we propose will solve this problem through a continuous and efficient content-based growing of the initially static clustering. We have developed an additional structure -- the C-Tree -- which stores the knowlege of the hierarchical clustering component, i.e. hierarchical Growing Neural Gas (GNG), for unsupervised content based classifica...

• Type: Journal
• Year: 2009
• Where: GVD
• Authors: Sebastian Leuoth, Wolfgang Benn
• Keywords: Database, Dynamic Insert Strategy, GVD 2009, Hierarchical Clustering Component, Value-based Interval Methods