Usually used approaches for non-hierarchical clustering of data are well known k-means or k-medoids methods. However, these fundamental methods are poorly applicable in situations where number of clusters is almost unpredictable. Formerly, they were adapted to allow splitting and merging when some defined criterion is met. On the other hand there are also methods based on artificial neural netw...

2 Introduction This document demonstrates the functionality of the flowType package for phenotyping FCM assays. flowType uses a simple threshold, Kmeans, flowMeans or flowClust to partition every channel to a positive and a negative cell population. These partitions are then combined to generate a set of multi-dimensional phenotypes. For more details on how this package can be used in a complet...

Most partition-based cluster analysis methods (e.g., kmeans) will partition any dataset D into k subsets, regardless of the inherent appropriateness of such a partitioning. This paper presents a family of permutation-based procedures to determine both the number of clusters k best supported by the available data and the weight of evidence in support of this clustering. These procedures use one ...

A new clustering algorithm based on grid projections is proposed. This algorithm, called U*C, uses distance information together with density structures. The number of clusters is determined automatically. The validity of the clusters found can be judged by the U*-Matrix visualization on top of the grid. A U*-Matrix gives a combined visualization of distance and density structures of a high dim...

We investigate the role of the initialization for the stability of the kmeans clustering algorithm. As opposed to other papers, we consider the actual k-means algorithm and do not ignore its property of getting stuck in local optima. We are interested in the actual clustering, not only in the costs of the solution. We analyze when different initializations lead to the same local optimum, and wh...

Content-based image retrieval (CBIR) is a technique used for extracting similar images from an image database. CBIR system is required to access images effectively and efficiently using information contained in image databases. Here, K-Means is to be used for Image retrieval. The Kmeans method can be applied only in those cases when the mean of a cluster is defined. The K-means method is not su...

In this paper a handwritten character recognition algorithm based on structural characteristics, histograms and profiles, is presented. The wellknown horizontal and vertical histograms are used, in combination with the newly introduced radial histogram, outin radial and inout radial profiles for representing 32x32 matrices of characters, as 280dimension vectors. The Kmeans algorithm is used for...

This paper describes our recent research progress on generalizing triangle inequality (TI) to optimize Machine Learning algorithms that involve either vector dot products (e.g., Neural Networks) or distance calculations (e.g., KNN, KMeans). The progress includes a new form of TI named Angular Triangular Inequality, abstractions to enable unified treatment to various ML algorithms, and TOP, a co...

Vector Quantization can be considered as a data compression technique. In the last few years, vector quantization has been increasingly applied to reduce problem complexity like pattern recognition. In speech recognition, discrete systems are developed to build up real-time systems. This paper presents original results by comparing the KMeans and the Kohonen approaches on the same recognition p...

Received Nov 30, 2017 Revised Jan 15, 2018 Accepted Feb 4, 2018 In this paper, a review about different algorithm is proposed efficiently to segment the satellite images. Segmentation of Image is one of the promising and active researches in recent years. As literature prove that region segmentation will produce better results. Human visual perception is more effective than any machine vision s...