The main goal of this paper is to compare the performance which can be achieved by five different approaches analyzing their applications’ potentiality on real world paradigms. We compare the performance obtained with (1) Multi-network RBF/LVQ structure (2) Discrete Hidden Markov Models (HMM) (3) Hybrid HMM/MLP system using a Multi LayerPerceptron (MLP) to estimate the HMM emission probabilitie...

This work shows how concepts from the electromagnetic field theory can be efficiently used in clustering with constraints. The proposed framework transforms vector data into a fully connected graph, or just works straight on the given graph data. User constraints are represented by electromagnetic fields that affect the weight of the graph's edges. A clustering algorithm is then applied on the ...

Gene expression data analysis is playing a vital role in diagnosing the diseases and drug designing. Many researchers realized that most of the cancers could be diagnosed based on the gene expression data. This paper focusses on identifying the prominent genes, which are mainly causing the Leukemia cancer using computational methods. Clustering methods are used to identify the components of a d...

To address the problems of lack of training data and difficult to find optimal value in information security risk assessment, this paper applying a new information measure method and fuzzy clustering in information security risk assessment. The new method quantifies risk factors of all data and the dependence degree of safety with the mutual information computing. Then search optimal points in ...

Automatic beat tracking and tempo estimation are challenging tasks, especially for audio music with nonbinary tempo or weak percussion. This paper proposes a Kmeans clustering approach to handle tempo estimation with one-third/triple tempo or weak percussion. In particular, the first stage is to compute the tempo curve from the tempogram by DP(Dynamic Programming). Then use Kmean clustering to ...

Abstract— to design an image transformation system is Depending on the transform chosen, the input and output images may appear entirely different and have different interpretations. Image Transformation with the help of certain module like input image, image cluster index, object in cluster and color index transformation of image. K-means clustering algorithm is used to cluster the image for b...

-----------------------------------------------------------------ABSTRACT-------------------------------------------------------The existing clustering algorithm has a sequential execution of the data. The speed of the execution is very less and more time is taken for the execution of a single data. A new algorithm Parallel Implementation of Genetic Algorithm using KMeans Clustering (PIGAKM) is...

While students’ skill set profiles can be estimated with formal cognitive diagnosis models [8], their computational complexity makes simpler proxy skill estimates attractive [1, 4, 6]. These estimates can be clustered to generate groups of similar students. Often hierarchical agglomerative clustering or k-means clustering is utilized, requiring, for K skills, the specification of 2K clusters. T...

Image retrieval system is an active area to propose a new approach to retrieve images from the large image database. In this concerned, we proposed an algorithm to represent images using divisive based and partitioned based clustering approaches. The HSV color component and Haar wavelet transform is used to extract image features. These features are taken to segment an image to obtain objects. ...

Extreme learning machine (ELM), used for the “generalized” single-hidden-layer feedforward networks (SLFNs), is a unified learning platform that can use a widespread type of feature mappings. In theory, ELM can approximate any target continuous function and classify any disjoint regions; in application, many experiment results have already demonstrated the good performance of ELM. In view of th...