Analysis of Clustering technique

Data mining technique has been considered as useful means for recognize patterns and accumulate of large set of data. This method is basically used to extract the unknown pattern from the large set of data as real time applications. It is an approximate intellect discipline which has appeared valuable tool for data analysis, new knowledge recognition and independent decision making. The speech recognition is also the most important research area to find the speech signal by the computer. To evolve the recognition of the continuous speech signal, a speech segmentation, feature extraction and clustering techniques are used. The unlabelled data from the large dataset can be categorized initially in an unaided fashion by using cluster analysis. The result of the clustering process and efficiency of its application are generally resolved through algorithms. There are various algorithms which are used to solve this problem. In this research paper two important clustering algorithms namely canter points based K-Means and representative object based FCM (Fuzzy C-Means) clustering algorithms are compared. The Hidden morkov model and Gaussian mixture model are the most suitable acoustic models are used to scale the continuous speech signal and recognize the corresponding text data. Priyanka Jadhav | Rasika Patil"Analysis of Clustering technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15616.pdf http://www.ijtsrd.com/computer-science/data-miining/15616/analysis-of-clustering-technique/priyanka-jadhav