A Novel Distance Similarity Measure on Learning Techniques & Comparison with Image Processing

–Clustering techniques make it possible to search large amounts of data for characteristic rules and patterns. To monitoring each data recorded on a cluster or any data mining classification, they can be used to calculate the distance similarity. In this paper, we present “Supervised & Unsupervised learning” a method similarity measures which are used for analysis. The clustering method first partitions the training instances into two clusters using Euclidean distance similarity, on each cluster, representing a density region. To analyze any technique in the mining, our work studies the best measure by using classification association with supervised & unsupervised algorithms that have not been used before. We compare the Euclidean distance similarity image processing that have the best efficiency or the best learning. Keywords––Distance Similarity, Measure, Metric, Mining Classifications, Euclidean, Huffman Code.