The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of k-based clustering processes like k-nearest neighbour and k-means clustering. The value of k in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding K’s optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques.

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Customers are assets for business. The companies are investing more for customer relationship management. Retaining customer for long time is a difficult process in today’s trend. On line shopping is also increasing day by day. People are more interested to visit popular web sites and they are spending very less time to choose their products. On line shops are paying more interest to analyze customer preferences, their needs, shopping behaviors through data mining technique. Proper classification is necessary for organizing such data. In this work, Customer with the same buying behavior is grouped based on the features age and salary. K-Means algorithm is applied to form clusters with different K values for original data and normalized data. The within sum of square (wss) is calculated for both the data for different cluster size. The minimum wss is considered to be better which is achieved in normalized data. The validity of cluster is evaluated by elbow, silhouette and gap statistic method to choose the optimal number of clusters. This work is implemented in R software.

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