Clustering is a process of putting similar data into groups. This paper presents data clustering using improved genetic algorithm (IGA) in which an efficient method of crossover and mutation are implemented. Further it is hybridized with the popular NelderMead (NM) Simplex search and K-means to exploit the potentiality of both in the hybridized algorithm. The performance of hybrid approach is e...

Fuzzy sets and rough sets have been widely used in many clustering algorithms for medical image segmentation, and have recently been combined together to better deal with the uncertainty implied in observed image data. Despite of their wide spread applications, traditional hybrid approaches are sensitive to the empirical weighting parameters and random initialization, and hence may produce less...

This paper introduces a hybrid hierarchical clustering method, which is a novel method for speeding up agglomerative hierarchical clustering by seeding the algorithm with clusters obtained from K-means clustering. This work describes a benchmark study comparing the performance of hybrid hierarchical clustering to that of conventional hierarchical clustering. The two clustering methods are compa...

As medical images contain uncertainties, there are difficulties in classification of images into homogeneous regions. Fuzzy sets, rough sets and the combination of fuzzy and rough sets plays a prominent role in formalizing uncertainty, vagueness, and incompleteness in diagnosis. Development of hybrid approaches for the segmentation of the magnetic resonance imaging (MRI) with the ability of com...

Clustering hashtags based on their semantics is an important problem with many applications. The uncontrolled usage of hashtags in social media, however, makes the quality of semantics and the frequency of usage vary a lot, and this poses 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 hasht...

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

There is a steadily increasing need for autonomous systems that must be able to function with minimal human intervention to detect and isolate faults, and recover from such faults. In this paper we present a novel hybrid Model based and Data Clustering (MDC) architecture for fault monitoring and diagnosis, which is suitable for complex dynamic systems with continuous and discrete variables. The...

We describe the participation of the CERTH/CEA-LIST team in the MediaEval 2016 Placing Task. We submitted five runs to the estimation-based sub-task: one based only on text by employing a Language Model-based approach with several refinements, one based on visual content, using geospatial clustering over the most visually similar images, and three based on a hybrid scheme exploiting both visual...

Clustering is a significant mechanism used in Wireless Sensor Networks in order to have an efficient energy balance which is inevitable to prolong the lifetime. The concept of unequal clustering has proved to be an effective method for load balancing and thereby reducing hotspot issues in the energy constrained wireless sensor networks. This paper proposes an energy efficient clustering mechani...