قدم في هذا البحث تعديل لخوارزمية عنقدة البيانات الMountain الضبابية, حيث
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
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
Wireless sensor network simulation programs provide representation for an actual system, without needing to deploy real testbed which is highly constrained by the available budget, and the direct operations inside physical layer in most of these prog