Effectiveness of Heuristic Based Approach on the Performance of Indexing and Clustering of High Dimensional Data

Data in practical applications (e.g., images, molecular biology, etc) is mostly characterised by high dimensionality and huge size or number of data instances. Though, feature reduction techniques have been successful in reducing the dimensionality for certain applications, dealing with high dimensional data is still an area which has received considerable attention in the research community. Indexing and clustering of high dimensional data are two of the most challenging techniques that have a wide range of applications. However, these techniques suffer from performance issues as the dimensionality and size of the processed data increases. In our effort to tackle this problem, this paper demonstrates a general optimisation technique applicable to indexing and clustering algorithms which need to calculate distances and check them against some minimum distance condition. The optimisation technique is a simple calculation that finds the minimum possible distance between two points, and checks this distance against the minimum distance condition; thus reusing already computed values and reducing the need to compute a more complicated distance function periodically. Effectiveness and usefulness of the proposed optimisation technique has been demonstrated by applying it with successful results to clustering and indexing techniques. We utilised a number of clustering techniques, including the agglomerative hierarchical clustering, k-means clustering, and DBSCAN algorithms. Runtime for all three algorithms with this optimisation scenario was reduced, and the clusters they returned were verified to remain the same as the original algorithms. The optimisation technique also shows potential for reducing runtime by a substantial amount for indexing large databases using NAQ-tree; in addition, the optimisation technique shows potential for reducing runtime as databases grow larger both in dimensionality and size.