Background:
Clustering is one of the important techniques in Data Mining to group the related data. Clustering can be applied on numerical data as well as web objects such as URLs, websites, documents, keywords etc. which is
the building block for many recommender systems as well as prediction models.
Objective:
The objective of this research article is to develop an optimal clustering approach which considers semantics of
web objects to cluster them in a group. More so importantly, the purpose of the proposed work is to strictly improve the
computation time of clustering process.
Methods:
In order to achieve the desired objectives, following two contributions have been proposed to improve the clustering approach 1) Semantic Similarity Measure based on Wu-Palmer Semantics based similarity 2). Two-Level Densitybased Clustering technique to reduce the computational complexity of density based clustering approach.
Results:
The efficacy of the proposed method has been analyzed on AOL search logs containing 20 million web queries.
The results showed that our approach increases the F-measure, and decreases the entropy. It also reduces the computational complexity and provides a competitive alternative strategy of semantic clustering when conventional methods do
not provide helpful suggestions.
Conclusion:
A clustering model has been proposed which is composed of two components i.e. Similarity measure and
Density based two-level clustering technique. The proposed model reduced the time cost of density based clustering approach without effecting the performance.
Rail Steel Health Analysis Based on a Novel Genetic Density-based Clustering Technique and Manifold Representation of Acoustic Emission Signals
