Bidirectional Agglomerative Hierarchical Clustering using AVL Tree Algorithm
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.
Keywords: Hierarchical, Clustering, Bidirectional algorithm, Agglomerative, AVL tree
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ABOUT THE AUTHORS
Hussain Abu-Dalbouh
Hussain Mohammad Abu-Dalbouh is a PhD student at the University Science Islam Malaysia (USIM), was born on the 26 of May 1982, his nationality Jordanian. He obtained his Bachelor’s degree in Computer Information System in 2005 from the Al Yarmouk University, Jordan. He received his Master’s degree in Information Technology from University Utara Malaysia (UUM) in 2009. His interest Areas: Artificial Intelligence (AI), Data Mining (DM), Visualization, Tree data structure, Data structure and algorithms.
Norita Md Norwawi
Norita Md Norwawi is an Associate Professor at Universiti Sains Islam Malaysia. She obtained her Bachelor in Computer Science in 1987 from the University of New South Wales, Australia. She received her Master’s degree in Computer Science from National University of Malaysia in 1994. In 2004, she obtained her PhD specializing in Temporal Data Mining and Multiagent System from University Utara Malaysia. As an academician, her research interests include artificial intelligence, multi-agent system, temporal data mining, text mining, knowledge mining, information security and digital Islamic application and content. Her works have been published in international conferences, journals and won awards on research and innovation competition in national and international level.
Hussain Abu-Dalbouh
Hussain Mohammad Abu-Dalbouh is a PhD student at the University Science Islam Malaysia (USIM), was born on the 26 of May 1982, his nationality Jordanian. He obtained his Bachelor’s degree in Computer Information System in 2005 from the Al Yarmouk University, Jordan. He received his Master’s degree in Information Technology from University Utara Malaysia (UUM) in 2009. His interest Areas: Artificial Intelligence (AI), Data Mining (DM), Visualization, Tree data structure, Data structure and algorithms.
Norita Md Norwawi
Norita Md Norwawi is an Associate Professor at Universiti Sains Islam Malaysia. She obtained her Bachelor in Computer Science in 1987 from the University of New South Wales, Australia. She received her Master’s degree in Computer Science from National University of Malaysia in 1994. In 2004, she obtained her PhD specializing in Temporal Data Mining and Multiagent System from University Utara Malaysia. As an academician, her research interests include artificial intelligence, multi-agent system, temporal data mining, text mining, knowledge mining, information security and digital Islamic application and content. Her works have been published in international conferences, journals and won awards on research and innovation competition in national and international level.
