• Ms.A.Uma maheswari
• Dr N Revathy

Clustering is a well-known problem in statistics and engineering, namely, how to arrange a set of vectors (measurements) into a number of groups (clusters). Clustering is an important area of application for a variety of fields including data mining, statistical data analysis and vector quantization. The problem has been formulated in various ways in the machine learning, pattern recognition optimization and statistics literature. The fundamental clustering problem is that of grouping together (clustering) data items that are similar to each other. The most general approach to clustering is to view it as a density estimation problem. Classification algorithms rely on human supervision to train it to classify data into pre-defined categorical classes. The term “classification” is frequently used as an algorithm for all data mining tasks. Instead, it is best to use the term to refer to the category of supervised learning algorithms used to search interesting data patterns. While classification algorithms have become very popular and ubiquitous in DM research, it is just but one of the many types of algorithms available to solve a specific type of DM task.

Clustering, Scalability, Agglomerative clustering.

"SCALABILITY ANALYSIS THROUGH AGGLOMERATIVE (HIERARCHICAL) CLUSTERING – COMPLETE AND CENTROID ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.742-754, November-2018, Available :http://www.jetir.org/papers/JETIR1811A94.pdf

"SCALABILITY ANALYSIS THROUGH AGGLOMERATIVE (HIERARCHICAL) CLUSTERING – COMPLETE AND CENTROID ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp742-754, November-2018, Available at : http://www.jetir.org/papers/JETIR1811A94.pdf