• العربية
    • English
  • English 
    • العربية
    • English
  • Login
Home
Publisher PoliciesTerms of InterestHelp Videos
Submit Thesis
IntroductionIUGSpace Policies
JavaScript is disabled for your browser. Some features of this site may not work without it.
Search 
  •   Home
  • Faculty of Engineering
  • Staff Publications- Faculty of Engineering
  • Search
  •   Home
  • Faculty of Engineering
  • Staff Publications- Faculty of Engineering
  • Search

Search

Show Advanced FiltersHide Advanced Filters

Filters

Use filters to refine the search results.

Now showing items 1-10 of 10

  • Sort Options:
  • Relevance
  • Issue Date Asc
  • Issue Date Desc
  • Results Per Page:
  • 5
  • 10
  • 20
  • 40
  • 60
  • 80
  • 100
Thumbnail

Integrating Bat Algorithm to Inverse Weighted K-means

Alhanjouri, Mohammed A.; Alghoul, Ahmed (Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, 2019-07-30)
Inverse Weighted K-means less sensitive to poorinitialization than the traditionalK-means algorithm. Therefore, this paper introduce a new hybrid algorithm that integrates inverse weighted k-means algorithm ...
Thumbnail

Clustering Cells Shape Descriptors Using K-Means vs. Genetic Algorithm

Abushmmala, Faten; Alhanjouri, Mohammed A. (International Technology and Science Publications (UK), 2018)
This paper interested in clustering red blood cells, these cells are in form of digital images of blood films, a comparison made between Genetic Algorithm (GA) and K-Means behavior/performance in clustering. The data set ...
Thumbnail

Improving Bregman k-means

Ashour, Wesam M.; Fyfe, Colin (Inderscience Publishers Ltd, 2014)
We review Bregman divergences and use them in clustering algorithms which we have previously developed to overcome one of the difficulties of the standard k-means algorithm which is its sensitivity to initial conditions ...
Thumbnail

Color based image segmentation using different versions of k-means in two spaces

Abu Shmmala, Faten; Ashour, Wesam M. (2013)
In this paper color based image segmentation is done in two spaces. First in LAB color space and second in RGB space all that done using three versions of K-Means: K-Means, Weighted K-Means and Inverse Weighted K-Means ...
Thumbnail

Colour Based Segmentation of Red Blood Cells using K-means and Image Morphological Operations

Abushmmala, Faten; Alhanjouri, Mohammed A. (INTERNATIONAL JOURNAL OF ADVANCED AND INNOVATIVE RESEARCH, 2013)
This paper interested in clustering red blood cells, these cells are in form of digital images of blood films, a comparison made between Genetic Algorithm (GA) and K-Means behavior/performance in clustering. The data set ...
Thumbnail

K-means algorithm with a novel distance measure

Abudalfa, Shadi; Mikki, Mohammad (The Scientific and Technological Research Council of Turkey, 2013)
In this paper, we describe an essential problem in data clustering and present some solutions for it. We investigated using distance measures other than Euclidean type for improving the performance of clustering. We also ...
Thumbnail

Stemming effectiveness in clustering of arabic documents

Ghanem, Osama A; Ashour, Wesam M. (Foundation of Computer Science, 2012)
Clustering is an important task gives good results with information retrieval (IR), it aims to automatically put similar documents in one cluster. Stemming is an important technique, used as feature selection to reduce ...
Thumbnail

Avoiding objects with few neighbors in the K-Means process and adding ROCK Links to its distance

Alnabriss, Hadi A; Ashour, Wesam M. (International Journal of Computer Applications, 244 5 th Avenue,# 1526, New York, NY 10001, USA India, 2011)
K-means is considered as one of the most common and powerful algorithms in data clustering, in this paper we're going to present new techniques to solve two problems in the K-means traditional clustering algorithm, the 1st ...
Thumbnail

A Novel Clustering Algorithm using K-means (CUK). The Islamic

Alnaji, Khaled W.; Ashour, Wesam M. (Foundation of Computer Science, 2011)
While K-means is one of the most well known methods to partition data set into clusters, it still has a problem when clusters are of different size and different density. K-means converges to one of many local minima. Many ...
Thumbnail

A novel construction of connectivity graphs for clustering and visualization

Barbakh, Wesam; Fyfe, Colin (World Scientific and Engineering Academy and Society (WSEAS), 2008)
We [5, 6] have recently investigated several families of clustering algorithms. In this paper, we show how a novel similarity function can be integrated into one of our algorithms as a method of performing clustering and ...

The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Contact Us | Send Feedback
 

 

Browse

All of IUGSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsSupervisorsThis CollectionBy Issue DateAuthorsTitlesSubjectsSupervisors

My Account

LoginRegister

Discover

AuthorAshour, Wesam M. (6)Alhanjouri, Mohammed A. (3)Abushmmala, Faten (2)Fyfe, Colin (2)Abu Shmmala, Faten (1)Abudalfa, Shadi (1)Alghoul, Ahmed (1)Alnabriss, Hadi A (1)Alnaji, Khaled W. (1) Ghanem, Osama Abedl Fattah (1)... View MoreSubject
k-means (10)
clustering (3)cells shape descriptors (2)cells shapes (2)genetic algorithm (2)arabic text clustering (1)average m ean distance (1)Bat algorithm (1)bregman divergences (1)centroids' initialization robust k-means (1)... View MoreDate Issued2010 - 2019 (9)2008 - 2009 (1)

The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Contact Us | Send Feedback