Paper Title
Modified Agglomerative Hierarchical Clustering (MAHC) Algorithm For Document Clustering

In today’s world huge amount of knowledge is propagated and stored in text databases over the large network. This leads to increment in the numbers of document files. So, a vigorous and skilful way is needed to group this large amount of data. Clustering is the finest tool of data mining for regulating and harmonizing information. Clustering outlines similar objects or data into one cluster and different objects into another one based on their measurement of diminishing inter similarity and overestimating intra dissimilarity. However, most of clustering techniques face many issues like high dimensionality, scalability, accuracy, etc. This paper presents a modified algorithm based on Agglomerative Hierarchical Clustering (AHC) method to improve the quality of clustering. Many traditional document clustering systems consider termfrequency when creating data martrix. Unlike existing systems, the proposed algorithm considers the items instead of term or word-frequency. So, the proposed algorithm can merge the similar clusters efficiently into the same cluster, so it can increase the quality of clustering and decrease the processing time than the original AHC method. Keywords: Agglomerative hierarchical clustering algorithm, Document clustering, Modified AHC algorithm, Optimized bubble sort algorithm, Similarity measure.