Missing values in datasets should be extracted from the datasets or should be estimated before they are used for classification, association rules or clustering in the preprocessing stage of data mining. In this study, we utilize a fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm. In this method, the fuzzy clustering parameters, cluster si...

Query clustering is a task that groups similar queries automatically without using predetermined class descriptions. Such clusters can be used to discover the common interests of online information seekers to exploit their collective search experience for the benefit of others. Since similarity is fundamental to the definition of a cluster, measures of similarity between two queries is essentia...

Multiple hypothesis testing and clustering have been the subject of extensive research in high-dimensional inference, yet these problems usually have been treated separately. By defining true clusters in terms of shared parameter values, we could improve the sensitivity of individual tests, because more data bearing on the same parameter values are available. We develop and evaluate a hybrid me...

rainfall-runoff is one of complex hydrological processes that is affected by a variety of physical and hydrological factors. in this study statistical method armax model, neural network, neuro-fuzzy (anfis subtractive clustering and grid partition) and two hybrid models of this methods were used to simulate rainfall-runoff and prediction of streamflow. in each method optimum structure was deter...

The fuzzy c-means (FCM) has been a well-known algorithm in machine learning/data mining area as a clustering algorithm. It can also be used for image segmentation, but the algorithm is not robust to noise. The possibilistic c-means (PCM) algorithm was proposed to overcome such a problem. However, the performance of PCM is too sensitive to the initialization of cluster centers, and often deterio...

The uncontrolled usage of hashtags in social media makes them vary a lot in the quality of semantics and the frequency of usage. Such variations pose a challenge to the current approaches which capitalize on either the lexical semantics of a hashtag by using metadata or the contextual semantics of a hashtag by using the texts associated with a hashtag. This thesis presents a hybrid approach to ...

Deep Web database clustering is a key operation in organizing Deep Web resources. Cosine similarity in Vector Space Model (VSM) is used as the similarity computation in traditional ways. However it cannot denote the semantic similarity between the contents of two databases. In this paper how to cluster Deep Web databases semantically is discussed. Firstly, a fuzzy semantic measure, which integr...

Web service clustering is one of a very efficient approach to discover Web services efficiently. Current approaches use similarity-distance measurement methods such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information, shortage of high-quality ontologies and encoding fine...