Robust signal processing compression for clustering of speech waveform and image spectrum

We address the issue regarding big data of high dimensional computation. Various statistical and signal processing techniques are used for compression and dimension reduction in last decade for data mining research. Most prominent technique in recent trend of computation for feature extraction is PCA,ICA, MDS, LDA, DWT, DFT and S-transform. These techniques are being used in different dimension of research arena. In this paper we have tried to dissect wavelet transform in speech processing, image processing with respect to time and frequency domain. After noise free decomposition the observation or samples correlation and similarity computation performed to form cluster. The cluster analysis is based on k-means cluster.