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Nirmaladevi, N.
- Identifying Outliers in Datasets Using Outlier Removal Clustering (ORC) Algorithm
Authors
1 Department of Computer Science, Sree Saraswathi Thyagaraja College, Thippampatti, Pollachi, IN
Source
Biometrics and Bioinformatics, Vol 6, No 7 (2014), Pagination: 182-185Abstract
The objective function of general K-Mean, this work associates a weight vector with each cluster to indicate which dimensions are relevant to the clusters. To prevent the value of the objective function from decreasing because of the elimination of dimensions, virtual dimensions are added to the objective function. The values of data points on virtual dimensions are set artificially to ensure that the objective function is minimized when the real subspace clusters or the clusters in original space are found. The outlier detection problem in some cases is similar to the classification problem. For example, the main concern of clustering-based outlier detection algorithms is to find clusters and outliers, which are often regarded as noise that should be removed in order to make more reliable clustering. This research work presents an algorithm that provides outlier detection and data clustering simultaneously. The algorithm improves the estimation of centroids of the generative distribution during the process of clustering and outlier discovery.Keywords
Data Mining, Clustering, K-Means, High Dimensions, Outlier Removal Clustering (ORC) Algorithm.
- Performance Analysis of Healthy Diet Recommendation System Using Web Data Mining
Authors
1 Department of Computer Science, Sree Saraswathi Thyagaraja College, Thippampatti, IN
2 Department of Master of Computer Applications, Sree Saraswathi Thyagaraja College, Thippampatti, IN
Source
Biometrics and Bioinformatics, Vol 6, No 7 (2014), Pagination: 189-191Abstract
Medical study has revealed that people set a bigger possibility of countering free radicals and warding off illness by consumption of healthy foods and by increasing their resistant system. Due to the poor eating habits people suffer from many diseases. In the current scenario fast food become important food in daily routine because it is effortlessly available but taking fast food in routine may cause for disease like heart attack, diabetics etc. Healthier diets help us to maintain our health and keep us away from many diseases. For better recovery from diseases or surgery etc individual have special needs according to their medical profile, cultural backgrounds and nutrient requirements. Design and implementation of healthy diet recommendation system is based on web data mining which is t he application of data mining technique help us to determine pattern from web. In terms of accuracy and time performance analysis of recommendation system using two decision tree learning algorithm.