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Register a new userModifying Mountain Clustering Algorithm and Using It to Enhance the Performance of Fuzzy C-Means Algorithm
تعديل خوارزمية العنقدة ال Mountain و استخدامها لتحسين أداء خوارزمية ال C-Means الضبابية
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نقدم في هذا البحث تعديل لخوارزمية عنقدة البيانات الMountain الضبابية, تمكنا من جعل هذه الخوارزمية تعمل بشكل آلي, و ذلك من خلال إيجاد طريقة لتقسيم الفضاء و تحديد قيم وسطاء الدخل و شرط التوقف آلياً بدلاً من إدخالها من قبل المستخدم.
In this paper, we introduce a modification to fuzzy mountain data clustering algorithm. We were able to make this algorithm working automatically, through finding a way to divide the space, to determine the values of the input parameters, and the stop condition automatically, instead of getting them by the user.
References used
YANG. M, AND WU. K, 2005- A Modified Mountain Clustering Algorithm, Published online:24 June 2005, London, p 125–138
CHIU. S, 1994- Fuzzy Model Identification Based on Cluster Estimate, journal of Intelligent and Fuzzy System, California, vol. 2, p 267-278
BERNETI. S, 2011- Design of Fuzzy Subtractive Clustering Model using Particle Swarm Optimization for the Permeability Prediction of the Reservoir, Islamic Azad University, Sari, Iran, Volume 29– No.11, September
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In this paper, we introduce a modification to fuzzy mountain
data clustering algorithm. We were able to make this algorithm
working automatically, through finding a way to divide the
space, to determine the values of the input parameters, and
the stop condition automatically, instead of getting them by the
user.
This paper introduces a new algorithm to solve some problems
that data clustering algorithms such as K-Means suffer from.
This new algorithm by itself is able to cluster data without the
need of other clustering algorithms.
In this research, a hybrid system was proposed between the
genetic algorithm and the fuzzy Kohonen clustering network ,
where the genetic algorithm is one of the methods of artificial
intelligence is one of the modern methods.
Nowadays, wireless networks are spreading more and more. The majority of installed networks have become wireless due to the simplicity of installation; where they do not need an infrastructure. This does not mean that the role of the wired networks i
s being eliminated. Instead, the wireless networks
are considered as a complementary of the wired networks. With all types of networks from personal and local area networks (PAN and LAN) to wide area networks (WAN) especially the Internet,
research has become oriented to focus on the quality of service (QoS) and the integration among all these networks taking into account the Internet which is considered as the backbone for each network that wants to exchange the information with any other network all over the world. In our research, we take into account the quality of service in the broadband networks such as the WiMax network (Worldwide Interoperability for Microwave Access) with IEEE 802.16e standard which covers cities and supports the mobility. This network can be used to interconnect the rural zones with the center of cities, this kind is called point-to-point, or it can be used to cover the cities and is called point-tomultipoint,
The last one is used to interconnect different wireless networks especially the local one which has infrastructure (Wi-fi: Wireless Fidelity) and networks which have many users and called
hotspots. However, the cells of Wi Max in the cities are called hotzones.
We propose a system model that performs the load balancing process between the base stations of WiMax network. This means, the proposed load balancing algorithm exchanges the terminals between the adjacent base stations in order to make the throughput in each base station equals to the throughput
in the others. This will improve the performance of the overall network and increase the available bandwidth for each terminal; in addition, this will increase the number of terminals which can be
served. On one side, these advantages return to the subscribers, they also return to the operator on the other side, not to mention the good renown that the operator will get from subscribers that will make more subscribers join to this network.
The proposed load balancing system can be centralized; implemented in a centralized server connected
to all base stations or distributed system implemented in each base station. The load balancing algorithm which consists of several steps is placed in a controller that achieves it. The load balancing process and the handover procedure have to be fast enough in order to prevent the adverse effect on
the quality of service especially for the real-time applications users.
The algorithm classifies objects to a predefined number of clusters, which is given by the user (assume k clusters). The idea is to choose random cluster centers, one for each cluster. These centers are preferred to be as far as possible from each ot
her. Starting points affect the clustering process and results. Here the Centroid initialization plays an important role in determining the cluster assignment in effective way. Also, the convergence behavior of clustering is based on the initial centroid values assigned. This research focuses on the assignment of cluster centroid selection so as to improve the clustering performance by K-Means clustering algorithm. This research uses Initial Cluster Centers Derived from Data Partitioning along the Data Axis with the Highest Variance to assign for cluster centroid.
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