Data mining techniques can be used to discover useful patterns by exploring and analyzing data and it’s feasible to synergistically combine machine learning tools to discover fuzzy classification rules. In this paper, an adaptive neuro fuzzy network with TSK fuzzy type and an improved quantum subtractive clustering has been developed. Quantum clustering (QC) is an intuition from quantum mechani...

The challenging issue in microarray technique is to analyze and interpret the large volume of data. This can be achieved by clustering techniques in data mining. In hard clustering like hierarchical and k-means clustering techniques, data is divided into distinct clusters, where each data element belongs to exactly one cluster so that the out come of the clustering may not be correct in many ti...

In this note we formulate image segmentation as a clustering problem. Feature vectors, extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of Kohonen learning vector quantization (LVQ) which integrates the Fuzzy cMeans (FCM) model with the learning rate and updating strategies of the LVQ Is used for this task. This network, which segmen...

In this note we formulate image segmentation as a clustering problem. Feature vectors, extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of Kohonen learning vector quantization (LVQ) which integrates the Fuzzy cMeans (FCM) model with the learning rate and updating strategies of the LVQ Is used for this task. This network, which segmen...

Many image segmentation techniques have been developed over the past two decades for segmenting the images, which help for object recognition, occlusion boundary estimation within motion or stereo systems, image compression, image editing. In this, there is a combined approach for segmenting the image. By using histogram equalization to the input image, from which it gives contrast enhancement ...

The aim of this research is to analyze ANFIS performance for prediction of financial time series data. Financial time series data is usually characterized by volatility clustering, persistence, and leptokurtic data behavior. The financial time series data are usually non-stationary and non-linear. ARIMA has a good performance to predict linear time series data, but its performance is decreasing...

Image segmentation is very essential and critical to image processing and pattern recognition. In this paper, a technique for color image segmentation called ‘Adaptive Neuro-Fuzzy Color Image Segmentation (ANFIS)’ is proposed. Adaptive Neuro-Fuzzy system is used for automatic multilevel image segmentation. This system consists of multilayer perceptron (MLP) like network that performs color imag...

This paper presents a novel hybrid data clustering algorithm based on parameter adaptive harmony search algorithm. The recently developed parameter adaptive harmony search algorithm (PAHS) is used to refine the cluster centers, which are further used in initializing Expectation-Maximization clustering algorithm. The optimal number of clusters are determined through four well-known cluster valid...

Underwater images suffers from low illumination and poor contrast due to refractions of light rays and poor visibility. Therefore, underwater image segmentation and object extraction is a difficult task. This paper proposed an efficient and fast underwater image segmentation method using thresholding with class 3 fuzzy Cmeans clustering and CLAHE enhancement method. CLAHE enhancement method is ...

In this note we formulate image segmentation as a clustering problem. Feature vectors, extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of Kohonen learning vector quantization (LVQ) which integrates the Fuzzy cMeans (FCM) model with the learning rate and updating strategies of the LVQ Is used for this task. This network, which segmen...