Scalability in VANET environment is an important issue of concern in recent years. Many approaches have been presented in the recent years and compared their merits and demerits with the existing approaches. In the present work a hybrid technique extension to the GPSR protocol is presented which is based on the content based filtering approach and the clustering approach. The filtering approach...

This papers presents a method to improve the recognition rate of hybrid connectionist/HMM speech recognition systems. At the same time this approach allows the easy introduction of context dependent models in the hybrid framework. The approach is based on a standard hybrid connectionist/HMM recognizer, in which the neural nets are trained to estimate the a posteriori probabilities for all phone...

This paper describes a novel data clustering algorithm, which is a hybrid approach combining a genetic algorithm with the classical c-means clustering algorithm (CMA). The proposed technique is superior to CMA in the sense that it converges to a nearby global optimum rather than a local one. As an application the problem of color image quantization is elaborated. Here, it is shown that substant...

0957-4174/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.eswa.2012.05.040 ⇑ Corresponding author. Tel.: +90 312 2977900. E-mail address: [email protected] (C.H. Alad In recent years, time series forecasting studies in which fuzzy time series approach is utilized have got more attentions. Various soft computing techniques such as fuzzy clustering, artificial neural net...

There is a huge amount of data which is being produced everyday in Information Technology industry but it is of no use until converted into useful information. Data mining is defined as the process of extracting of hidden predictive information from large databases. Data mining provides an easy and timesaving concept to extract the useful information from large database instead of going through...

Clustering is a way that classifies the raw data reasonably and searches the hidden patterns that may exist in datasets. It is a process of grouping data objects into disjoint clusters so that data in the same cluster are similar, and data belonging to different cluster are differ. Many algorithms have been developed for clustering. In this paper we are reviewing performance analysis of hybrid ...

In this paper, we propose an unsupervised hybrid framework for protein sequence clustering and classification which incorporates protein structural motif information. The proposed framework consists of three stages: protein structural motif scan, hybrid clustering, and sequence classification. The incorporation of protein structural motif detected by ScanProsite service provides a better measur...

In this paper, we propose an hybrid clustering based classification algorithm based on mean approach to effectively classify to mine the ordered sequences (paths) from weblog data in order to perform social network analysis. In the system proposed in this work for social pattern analysis, the sequences of human activities are typically analyzed by switching behaviors, which are likely to produc...

We present an integrated clinico-genomics environment. The proposed reference architecture provides for the seamless integration of clinical and genomic information, and aims towards the future genetic-medicine environment. Intelligent processing operations (i.e., data mining) are in the heart of this environment. In this context, we also present a novel graph-theoretic hybrid clustering approa...

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...