Please use this identifier to cite or link to this item:
|Title||Avoiding objects with few neighbors in the K-Means process and adding ROCK Links to its distance|
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 problem is its sensitivity for outliers, in this part we are going to depend on a function that will help us to decide if this object is an outlier or not, if it was an outlier it will be expelled from our calculations, that will help the K-means to make good results even if we added more outlier points; in the second part we are going to make K-means depend on Rock links in addition to its traditional distance, Rock links takes into account the number of common neighbors between two objects, that will make the K-means able to detect shapes that can't be detected by the traditional K-means.
|Published in||International Journal of Computer Applications|
|Series||Volume: 28, Number: 10|
|Publisher||International Journal of Computer Applications, 244 5 th Avenue,# 1526, New York, NY 10001, USA India|
|Item link||Item Link|
|Files in this item|
|Ashour, Wesam M._48.pdf||248.0Kb|
Showing items related by title, author, creator and subject.
Aldahdooh, Raed Tawfiq (الجامعة الإسلامية - غزة, 2013)Clustering is widely used to explore and understand large collections of data. K-means clustering method is one of the most popular approaches due to its ease of use and simplicity to implement. In this thesis, the researcher ...
Abushmmala, Faten Faraj Fadel (the islamic university, 2012)Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. Computer-aided diagnosis is another important application of pattern recognition, ...
Elhabbash, Abdessalam Hosain (the islamic university, 2010)Data clustering is an unsupervised classification method aims at creating groups of objects, or clusters, in such a way that objects in the same cluster are very similar and objects in different clusters are quite distinct. ...