In this study we evaluate with experiments three generic clustering algorithms, namely the Lowest-ID, the Highest Degree and the Extended Robust Re-clustering Algorithm which is the one proposed. The aim is to investi...
详细信息
In this paper we will introduce a new metric to compare two biological clusters. This metric measures both structural and node similarity of two clusters. We replace Neighborhood Affinity function by this metric and s...
详细信息
ISBN:
(纸本)9780889869042
In this paper we will introduce a new metric to compare two biological clusters. This metric measures both structural and node similarity of two clusters. We replace Neighborhood Affinity function by this metric and show that some of the previously known matched clusters are showing a very low structural similarity.
In order to handle the problem of linear separability in the early data clustering algorithms, Euclidean distance is being replaced with Kernel functions as measures of similarity. Another problem with the clustering ...
详细信息
This paper presents a study of clustering algorithms in bug classification for a company from a database that contains a description each bug. It is made a comparison these algorithms using a sample of the database of...
详细信息
In the research and development of intelligence system, clustering analysis is a very important problem. According to the new direct clustering algorithm using similarity measure of Vague sets as evaluation criteria p...
详细信息
To reduce speech recognition error rate we can use better statistical language models. These models can be improved by grouping words into word equivalence classes. clustering algorithms can be used to automatically d...
详细信息
To reduce speech recognition error rate we can use better statistical language models. These models can be improved by grouping words into word equivalence classes. clustering algorithms can be used to automatically do this word grouping. We present an incremental clustering algorithm and two iterative clustering algorithms. Also, we compare them with previous algorithms. The experimental results show that the two iterative algorithms perform as well as previous ones. It should be pointed out that one of them, that uses the leaving one out technique, has the ability to automatically determine the optimum number of classes. These iterative algorithms are used by the incremental one. On the other hand, the proposed incremental algorithm achieves the best results of the compared algorithms, its behavior is the most regular with the variation of the number of classes and can automatically determine the optimum number of classes.
clustering is basically an unsupervised learning technique to divide a collection of patterns into groups of similar objects based on a distance or similarity function. clustering techniques are applied in pattern cla...
详细信息
Text clustering is a popular research topic in the field of text mining, and now there are a lot of text clustering methods catering to different application requirements. Currently, Weibo data acquisition is through ...
详细信息
Online social networking streams have become an essential part of people’s life where people share their views, ideas, and almost all the events happening all around the globe are first reported on these microbloggin...
详细信息
Minimum spanning tree (MST) based clustering algorithms have been employed successfully to detect clusters of heterogeneous nature. Given a dataset of n random points, most of the MST-based clustering algorithms first...
详细信息
暂无评论