Open Access Open Access  Restricted Access Subscription or Fee Access

Improvement in Hierarchical Clustering to Reduce Execution Time and Improve Accuracy

Amandeep Kaur, Neena Madaan


This work presents an overview of the hierarchal clustering algorithm & various enhanced variations done on hierarchal clustering algorithm. Hierarchal is the basic algorithm used for discovering clusters within a dataset. The initial point selection effects on the results of the algorithm, both in the number of clusters found and their centroids. Methods to enhance the k-means clustering algorithm are discussed. With the help of these methods efficiency, accuracy, performance and computational time are improved. Some enhanced variations improve the efficiency and accuracy of algorithm. Basically in all the methods the main aim is to reduce the number of iterations which will decrease the computational time. Studies shows that hierarchal algorithm in clustering is widely used technique. Various enhancements done on hierarchal are collected, so by using these enhancements one can build a new hybrid algorithm which will be more efficient, accurate and less time consuming than the previous work.


Agglomerative, Clustering, Data Mining, Divisive, Hierarchical Clustering,

Full Text:



Jingxuan Li, Bo Shao, Tao Li, and Mitsunori Ogihara,” Hierarchical Co-Clustering: A New Way to Organize the Music Data”, 2012 IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 14, NO. 2

Yongkweon Jeon and Sungroh Yoon,” Multi-Threaded Hierarchical Clustering by Parallel Nearest-Neighbor Chaining”, 2013 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 24, NO. 1

Abdelghani Bellaachia, Erhan Guven, “Predicting Breast Cancer Survivability Using Data Mining Techniques”, Washington DC 20052, 2010

QASEM A. AL-RADAIDEH, ADEL ABU ASSAF 3EMAN ALNAGI, “Predictiong Stock Prices Using Data Mining Techniques”, the International Arab Conference on Information Technology (ACIT’2013)

Diwakar Meenakshi and Sushil Kumar,” Energy Efficient Hierarchical Clustering Routing Protocol for Wireless Sensor Networks”, 2012 CCSIT, Part I, LNICST

Xu-Qing Tang and Ping Zhu,” Hierarchical Clustering Problems and Analysis of Fuzzy Proximity Relation on Granular Space”, 2013 IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 21, NO. 5

Sanghoon Lee, and Melba M. Crawford,” Unsupervised Multistage Image Classification Using Hierarchical Clustering With a Bayesian Similarity Measure”, 2005 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 14, NO. 3

Azhar Rauf Mahfooz, Shah Khusro and Huma Javed “Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity”, Middle-East Journal of Scientific Research 12 (7): 959-963, 2012 ISSN 1990-92332012

K. A. Abdul Nazeer, M. P. Sebastian, “Improving the Accuracy and Efficiency of the k-means Clustering Algorithm, Proceedings of the World Congress on Engineering , Vol IWCE 2009, July 1 - 3, 2009, London, U.K

Osamor VC, Adebiyi EF, Oyelade JO and Doumbia S “Reducing the Time Requirement of K-Means Algorithm” PLoS ONE, Volume 7, Issue 12, pp-56-62, 2012.

Azhar Rauf, Sheeba, Saeed Mahfooz, Shah Khusro and Huma Javed, “Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity,” Middle-East Journal of Scientific Research, pages 959-963, 2012.

Kajal C. Agrawal and Meghana Nagori, “Clusters of Ayurvedic Medicines Using Improved K-means Algorithm”, International Conf. on Advances in Computer Science and Electronics Engineering, 2013

Stan Salvador and Philip Chan, “Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms”, 2010

Xiangnan Zhou1 , Shouping Zhang , Xinmin Xie , Mingxiang Yang, Yanjie Bi1 , Liqin Li, “Application of BP neutral networks to water demand prediction of Shenyang City based on principle component analysis”, 7th International Conference on Intelligent Computation Technology and Automation, 2014

Y. He, S. Blandin, L. Wynter and B. Trager, "Analysis and Real-Time Prediction of Local Incident Impact on Transportation Networks," Data Mining Workshop (ICDMW), 2014 IEEE International Conference on, Shenzhen, 2014, pp. 158-166


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.