This paper presents the fourth participation of the SINAI group, University of Jaén, in the Photo Retrieval task at Image CLEF 2009. Our system uses only the text of the queries, and a clustering system (based on kmeans) that combines different approaches based on a different use of the cluster data of the queries. The official results shown that the combination between the title of each query ...

The problems of finding alternative clusterings and avoiding bias have gained popularity over the last years. In this paper we put the focus on the quality of these alternative clusterings, proposing two approaches based in the use of negative constraints in conjunction with spectral clustering techniques. The first approach tries to introduce these constraints in the core of the constrained no...

background: liver cirrhosis was one of the most important liver diseases. other chronic liver diseases could be lead to liver cirrhosis. liver cirrhosis could be lead one kind of liver cancers named hepatocellular carcinoma. cirrhosis in the early stages just by laboratory and imaging testes could be diagnosed. in this study cirrhotic patients were classified based on laboratory symptoms. for t...

this paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (fpso) and fuzzy c-means (fcm) algorithms, to solve the fuzzyclustering problem, especially for large sizes. when the problem becomes large, thefcm algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. the pso algorithm does find ago...

In this study, the general ideas surrounding the k-medians problem are discussed. This involves a look into what k-medians attempts to solve and how it goes about doing so. We take a look at why k-medians is used as opposed to its k-means counterpart, specifically how its robustness enables it to be far more resistant to outliers. We then discuss the areas of study that are prevalent in the rea...

In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at...

slope stability analysis is an enduring research topic in the engineering and academic sectors. accurate prediction of the factor of safety (fos) of slopes, their stability, and their performance is not an easy task. in this work, the adaptive neuro-fuzzy inference system (anfis) was utilized to build an estimation model for the prediction of fos. three anfis models were implemented including g...

This paper compares clusters of aligned Persian and English texts obtained from k-means method. Text clustering has many applications in various fields of natural language processing. So far, much English documents clustering research has been accomplished. Now this question arises, are the results of them extendable to other languages? Since the goal of document clustering is grouping of docum...

This paper presents the first participation of the University of Ottawa group in the Photo Retrieval task at Image CLEF 2008. Our system uses the following components: Lucene for text indexing and LIRE for image indexing. We experiment with several clustering methods in order to retrieve images from diverse clusters. The clustering methods are: k-means clustering, hierarchical clustering, and o...