Driving cycle development for Kuala Terengganu city using k-means method

Probabilistic K-Means using Method of Moments

K-means is one of the most widely used algorithms for clustering in Data Mining applications, which attempts to minimize the sum of square of Euclidean distance of the points in the clusters from the respective means of the clusters. The simplicity and scalability of K-means makes it very appealing. However, K-means suffers from local minima problem, and comes with no guarantee to converge to t...

Urological Service at the General Hospital, Kuala Terengganu.

Using Life Cycle Assessment Method for Selecting Optimal Waste Management System in Tehran City

Introduction: In Tehran with a population of 9 million currently, about 2.5 million tons municipal solid waste have been producing annually. Materials and Models: In this study by using Life Cycle Assessment (LCA) model an optimal system of waste management of Tehran was recommended. Based on the quantity and quality of waste in Tehran in 2013 and facilities, three scenarios were selected. Fir...

Development of a Driving Cycle for Amman City with Performance Evaluation for Ice Vehicle

A good driving cycle is needed for accurate evaluation of a vehicle’s performance in terms of emission and fuel consumption. Driving cycles obtained for certain cities or countries are not usually applicable to other cities or countries. Therefore, considerable research has been conducted on developing driving cycles for certain cities and regions. In this paper, a driving cycle for a taxi in A...

A New Parallelization Method for K-means

K-means is a popular clustering method used in data mining area. To work with large datasets, researchers propose PKMeans, which is a parallel k-means on MapReduce [3]. However, the existing k-means parallelization methods including PKMeans have many limitations. It can’t finish all its iterations in one MapReduce job, so it has to repeat cascading MapReduce jobs in a loop until convergence. On...