Semantics Based Clustering through Cover-Kmeans with OntoVsm for Information Retrieval

Semantics-based Information Retrieval

In this paper we investigate the use of conceptual descriptions based on description logics for contentbased information retrieval and present several innovative contributions. We provide a query-byexamples retrieval framework which avoids the drawback of a sophisticated query language. We extend an existing DL to deal with spatial concepts. We provide a content-based similarity measure based o...

SVM-Kmeans: Support Vector Machine based on Kmeans Clustering for Breast Cancer Diagnosis

Breast cancer is the most common cancer in women, and is considered one of the most common causes of death. It increases by an alarming rate globally. Earlier detection and diagnosis could save lives and improve quality of life. In this paper, a new method for breast cancer diagnosis is presented. The proposed method, SVM-Kmeans, combines Kmeans, an unsupervised learning clustering technique, w...

Cooperative Information Retrieval Dialogues through Clustering

Novel centroid selection approaches for KMeans-clustering based recommender systems

Recommender systems have the ability to filter unseen information for predicting whether a particular user would prefer a given item when making a choice. Over the years, this process has been dependent on robust applications of data mining and machine learning techniques, which are known to have scalability issues when being applied for recommender systems. In this paper, we propose a k-means ...

A Semantics-based Information Retrieval Model

In this paper, I propose a semantics-based IR model, which uses triplet (concept-relationconcept) instead of keywords to index documents. It uses the sense instead of the string of a term to index document, which not only makes the representation more accuracy, but also well solves the synonym problem. More importantly, this model avoids the frequently occurred situation in keywordbased IR mode...