To maintain the quality of software and to better understand it, software architecture is decomposed. Software decomposition is done by various clustering methods. Each method provides different results of clustering on datasets. This paper presents the review of various clustering methods with A-KNN method in terms of efficiency and accuracy. It also proposes the way to enhance AKNN clustering...

The k nearest neighbors (kNN) graph, perhaps the most popular graph in machine learning, plays an essential role for graphbased learning methods. Despite its many elegant properties, the brute force kNN graph construction method has computational complexity of O(n), which is prohibitive for large scale data sets. In this paper, based on the divide-and-conquer strategy, we propose an efficient a...

OBJECTIVE To determine the relationship of bone marrow lesions (BMLs) with phenomena such as clinical symptoms, histological subchondral bone damage, and development of osteoarthritis, a reliable and reproducible method to localize and quantify BMLs accurately is indispensable. Therefore, the goal of the current study was to develop and validate a novel semiautomated segmentation method based o...

A vast majority of Loss Given Default (LGD) models are currently in use. Over all the years since new Capital Accord was published June 2004, there has been increasing interest modelling LGD parameter on part both academics and practitioners. The main purpose this paper is to propose estimation approaches that provide more effective results include unresolved cases procedure. motivation for pro...

To improve the class separability of Fisher linear discriminant analysis (FDA) for large category problems, we investigate the weighted Fisher criterion (WFC) by integrating weighting functions for dimensionality reduction. The objective of WFC is to maximize the sum of weighted distances of all class pairs. By setting larger weights for the most confusable classes, WFC can improve the class se...

Classification of spatial data has become important due to the fact that there are huge volumes of spatial data now available holding a wealth of valuable information. In this paper we consider the classification of spatial data streams, where the training dataset changes often. New training data arrive continuously and are added to the training set. For these types of data streams, building a ...