Means of positive numbers appear in many applications and have been a traditional matter study. In this work, we focus on defining new mean two values with some properties which are essential applications, ranging from subdivision multiresolution schemes to the numerical solution conservation laws. particular, three main crucial—in essence, ideas these roughly following: stay close minimum when...

Recording devices whether analog or digital, have traits which make them susceptible to noise. In selecting a noise reduction algorithm, one must weigh several factors. Image denoising is defined as a method to recover a true image from an observed noisy image and is applied in display systems to improve the quality of image. One of the popular denoising methods, NLM, produces the quality of im...

K-means (MacQueen, 1967) [1] is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set to a predefined, say K number of clusters. Determination of K is a difficult job and it is not known that which value of K can partition the objects as per our intuition. To overcome this probl...

In the arena of software, data mining technology has been considered as useful means for identifying patterns and trends of large volume of data. This approach is basically used to extract the unknown pattern from the large set of data for business as well as real time applications. It is a computational intelligence discipline which has emerged as a valuable tool for data analysis, new knowled...

Mini Batch K-means ([11]) has been proposed as an alternative to the K-means algorithm for clustering massive datasets. The advantage of this algorithm is to reduce the computational cost by not using all the dataset each iteration but a subsample of a fixed size. This strategy reduces the number of distance computations per iteration at the cost of lower cluster quality. The purpose of this pa...