Bipul Shyam Purkayastha

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Clustering is the process of organizing similar objects into groups, with its main objective of organizing a collection of data items into some meaningful groups. The problem of Clustering has been approached from different disciplines during the last few year's. Many algorithms have been developed in recent years for solving problems of numerical and(More)
This paper focuses on morphological analysis of Kokborok words to incorporate them into Kokborok dictionary and Kokborok Machine translator. So far, no attempt has been made to integrate the works for a concrete computational output. In this paper we particularly emphasize on bringing works on morphological analysis in the frame, with the goal to produce a(More)
There are many supervised clustering algorithms based on static datasets for finding their optimal clusters. Clustering is the task of organizing data into clusters such that the data objects that are similar to each other. For finding clusters of data stream of chunks, i.e. for dynamic clustering we proposed a incremental clustering algorithm which is a(More)
Words are characterized by its features. In an inflectional language, category of a word can be express by its tense, aspect and modality (TAM). Extracting features from an inflected word, one can categorised it with proper morphology. Hence features extraction could be a technique of part-of-speech (POS) tagging for morphologically inflected languages.(More)
In this paper, we have evaluated performance of various routing protocols for Vehicular Ad Hoc Network (VANET) in different traffic patterns with real vehicular traces. The vehicular movements are based on intelligent driver model with road intersection. Our objectives is to provide a comparative analysis among various ad hoc routing protocols based on(More)
However, there exist some flaws in classical K-means clustering algorithm. First, the algorithm is sensitive in selecting initial centroids and can be easily trapped at a local minimum with regards to the measurement (the sum of squared errors). Secondly, the KM problem in terms of finding a global minimal sum of the squared errors is NP-hard even when the(More)
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