We present an interdisciplinary approach to data clustering, based on algorithm originally developed for the Big Data Modelling of Sustainable Development Goals (BDMSDG). Its application context combines mechanics machine learning techniques with underlying pedagogical domain knowledge–unifying narratives scientists and educationists in searching potentially useful information historical data. ...

Abstract Convex clustering has received recently an increased interest as a valuable method for unsupervised learning. Unlike conventional methods such k-means, its formulation corresponds to solving convex optimization problem and hence, alleviates initialization local minima problems. However, while several algorithms have been proposed solve formulations, including those based on the alterna...

With rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. Clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. Because o...

using greedy clustering method to solve capacitated location-routing problem with fuzzy demands abstract in this paper, the capacitated location routing problem with fuzzy demands (clrp_fd) is considered. in clrp_fd, facility location problem (flp) and vehicle routing problem (vrp) are observed simultaneously. indeed the vehicles and the depots have a predefined capacity to serve the customerst...

One of the most important aspects of software project management is the estimation of cost and time required for running information system. Therefore, software managers try to carry estimation based on behavior, properties, and project restrictions. Software cost estimation refers to the process of development requirement prediction of software system. Various kinds of effort estimation patter...

The paper contains description of a new clustering methodology that partitions data set into clusters, such that regression indetermination coefficient for data from each cluster is minimized. A clustering algorithm that realizes this methodology with genetic programming approaches, as well as, some experimental results are presented. The application of the algorithm for planning cellular telep...

Combination strategies in classification are a popular way of overcoming instabilities in classification algorithms. A direct application of ideas such as “voting” to cluster analysis problems is not possible, as no a priori class information for the patterns is available. We present a methodology for combining ensembles of partitions obtained by clustering, discuss the properties of such combi...