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In this paper, we have implemented an efficient and novel technique for multi-label class prediction using associative rule mining. Many of the research works for the classification have been carried out on single-label datasets, but it is not useful for all real-world application accounting to multi-label datasets like scene classification, text categorization, etc. Hence, we propose an algorithm for performing multi-label classification and solve the problems which come across in the domain pertaining to single-label classification. Our novel technique (ARM-MLC) will aim in enhancing the accuracy of any decision-making processes. Here, in multi-label classification, based on our work, we aim to predict the multiple characters of the instances.

This paper proposes the use of a neural network based real time adaptive clustering* algorithm for the formation of a codebook of limited set of acoustical representation of finite set of vocal tract shapes from an articulatory space. Modified k-means algorithm (MKM) used for clustering nearly 10000 vocal tract shapes into 1000 cluster centers to form a codebook of articulatory shapes is computationally intensive for our application . An investigative study on the use of NN based algorithm over MKM algorithm at the peripheral level , for our application on Computer Aided Pronunciation-education, suggests the former for less intensive computation, with the possibility of improving the performance of the system by implementing the algorithm using a dedicated neural computer. In this paper, preliminary results of this study are reported.

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