Clustering algorithm is a widely used segmentation method in image processing applications. Vario... more Clustering algorithm is a widely used segmentation method in image processing applications. Variou s clustering based segmentation methods have been proposed. This paper presents an improved version of the Moving K-Means algorithm called New Enhanced Moving K-Means algorithm. In the proposed New Enhanced Moving K-Means algorithm, the distance measure used in the conventional Moving K-Means (i.e. eucledean distance) is enhanced. A comparative analysis of three clustering methods is presented. Three methods are: Moving K-means clustering algorithm, and Enhanced Moving KMeans algorithm. Clustering algorithms were evaluated on natural images and their performance is compared. Results demonstrate that Enhanced Moving K-Means algorithm is considered to be the most suitable techniques for image segmentation.
Now a day as the application area of technology increases the proportion of data and the nature a... more Now a day as the application area of technology increases the proportion of data and the nature also increases. One of such problem in data mining and machine learning techniques are class imbalance problem. Class imbalance problem is a problem where distribution of data largely belongs to one class while small or no data belongs to other class. Class imbalance problem is also known as skewed data problem. The data in real-world applicat ions often has imbalanced class distribution where most of the classifier correctly classifies majority class data while they completely ignore minority. This is the problem associated with class imbalance p roblem. There many techniques used to solve class imbalance problem such data preprocessing, algorithmic approach and ensemble techniques. Data preprocessing gives better solution than other techniques. Data preprocessing techniques broadly classified into oversampling and under sampling technique. The disadvantage associated with under sampling...
Clustering algorithm is a widely used segmentation method in image processing applications. Vario... more Clustering algorithm is a widely used segmentation method in image processing applications. Variou s clustering based segmentation methods have been proposed. This paper presents an improved version of the Moving K-Means algorithm called New Enhanced Moving K-Means algorithm. In the proposed New Enhanced Moving K-Means algorithm, the distance measure used in the conventional Moving K-Means (i.e. eucledean distance) is enhanced. A comparative analysis of three clustering methods is presented. Three methods are: Moving K-means clustering algorithm, and Enhanced Moving KMeans algorithm. Clustering algorithms were evaluated on natural images and their performance is compared. Results demonstrate that Enhanced Moving K-Means algorithm is considered to be the most suitable techniques for image segmentation.
Now a day as the application area of technology increases the proportion of data and the nature a... more Now a day as the application area of technology increases the proportion of data and the nature also increases. One of such problem in data mining and machine learning techniques are class imbalance problem. Class imbalance problem is a problem where distribution of data largely belongs to one class while small or no data belongs to other class. Class imbalance problem is also known as skewed data problem. The data in real-world applicat ions often has imbalanced class distribution where most of the classifier correctly classifies majority class data while they completely ignore minority. This is the problem associated with class imbalance p roblem. There many techniques used to solve class imbalance problem such data preprocessing, algorithmic approach and ensemble techniques. Data preprocessing gives better solution than other techniques. Data preprocessing techniques broadly classified into oversampling and under sampling technique. The disadvantage associated with under sampling...
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