Web-based AHP system

This chapter discusses about the development of a web-based multi criteria decision making system which implements Analytical Hierarchical Process (AHP) method in order to give the best decision/choice to decision makers. It is believed that such system will be able to offer more accurate and acceptable result based on a number of criteria and alternatives that the user has provided. The main objective of this chapter is to provide an understanding of how such a system is build and some idea on how actually Analytical Hierarchical Process is being implemented in the system. It is hoped that after reading this chapter, readers will get the general concept and idea on how similar systems work and function in the future

A guideline for using web-based AHP system

This chapter discusses the guidelines and step by step procedures in using the web-based multi criteria decision making using Analytical Hierarchical Process (AHP) method, which is presented in the previous chapter. Readers will be guided carefully starting from the development of the decision process towards the final decision itself, based on the criteria and choices supplied to the system. It is hoped that from this chapter, readers will be able to learn and understand the process flow of multi criteria decision making using Analytical Hierarchical Process and also be able to use the system without much difficulties, lnsha Alla

An Improved Density-Based Clustering Algorithm Using Gravity and Aging Approaches

Density-based clustering is one of the well-known algorithms focusing on grouping samples according to their densities. In the existing density-based clustering algorithms, samples are clustered according to the total number of points within the radius of the defined dense region. This method of determining density, however, provides little knowledge about the similarities among points. Additionally, they are not flexible enough to deal with dynamic data that changes over time. The current study addresses these challenges by proposing a new approach that incorporates new measures to evaluate the attributes similarities while clustering incoming samples rather than considering only the total number of points within a radius. The new approach is developed based on the notion of Gravity where incoming samples are clustered according to the force of their neighbouring samples. The Mass (density) of a cluster is measured using various approaches including the number of neighbouring samples and Silhouette measure. Then, the neighbouring sample with the highest force is the one that pulls in the new incoming sample to be part of that cluster. Taking into account the attribute similarities of points provides more information by accurately defining the dense regions around the incoming samples. Also, it determines the best neighbourhood to which the new sample belongs. In addition, the proposed algorithm introduces a new approach to utilize the memory efficiently. It forms clusters with different shapes over time when dealing with dynamic data. This approach, called Aging, enables the proposed algorithm to utilize the memory efficiently by removing points that are aged if they do not participate in clustering incoming samples, and consequently, changing the shapes of the clusters incrementally. Four experiments are conducted in this study to evaluate the performance of the proposed algorithm. The performance and effectiveness of the proposed algorithm are validated on a synthetic dataset (to visualize the changes of the clusters’ shapes over time), as well as real datasets. The experimental results confirm that the proposed algorithm is improved in terms of the performance measures including Dunn Index and SD Index. The experimental results also demonstrate that the proposed algorithm utilizes less memory, with the ability to form clusters with arbitrary shapes that are changeable over time