Bidirectional Agglomerative Hierarchical Clustering using AVL Tree Algorithm
Hussain Mohammad Abu-Dalbouh, Norita Md Norwawi, Bandar Baru Nilai · 2011
expressions or useful patterns from these large data. According to [2], never before in the history data has been generated at such high volumes as today. The hierarchy is often used to infer knowledge from groups of items and relations in varying granularities. Hierarchical clustering algorithms take an input of pairwise data-item similarities and output a hierarchy of the data-items. This paper presents Bidirectional agglomerative hierarchical clustering to create a hierarchy bottom-up, by iteratively merging the closest pair of data-items into one cluster. The result is a rooted AVL tree. The n leafs correspond to input data-items (singleton clusters) needs to n/2 or n/2+1 steps to merge into one cluster, correspond to groupings of items in coarser granularities climbing towards the root. As observed from the time complexity and number of steps need to cluster all data points into one cluster perspective, the performance of the bidirectional agglomerative algorithm using AVL tree is better than the current agglomerative algorithms. The experiment analysis results indicate that the improved algorithm has a higher efficiency than previous methods.