International Journals

Now showing 1 - 4 of 4

A SUPERLATIVE APPROACH FOR CLUSTERING TECHNIQUES-BRIEF STUDY
(MIT International Journal of Computer Science & Information Technology, 2013-08) T, Hashni; M, Divyavani
Clustering is the one of the foremost technique in the data mining and its applied in various areas such as artificial intelligence, bio-informatics, biology, computer vision, city planning, data mining, data compression, earth quake studies, image analysis, image segmentation, information retrieval, machine learning, marketing, medicine, object recognition, pattern recognition, spatial database analysis, statistics and web mining. Clustering means the act of partitioning an unlabelled dataset into groups of similar objects. The goal of clustering is to group sets of objects into classes such that similar objects are placed in the same cluster while dissimilar objects are in separate clusters. Over the past few years, several different types of biologically inspired algorithms have been proposed in the various domains. The ant-based clustering algorithms have received special attention from the community over the past few years for two main reasons. First, they are particularly suitable to perform exploratory data analysis and, second, they still require much investigation to improve performance, stability, convergence, and other key features that would make such algorithms mature tools for diverse applications. Ant-based clustering is a biologically inspired data clustering technique. These algorithms have recently been shown to produce good results in a wide variety of real-world applications. During the last five years, research on and with the ant-based clustering algorithms has reached a very promising state. In this paper, a brief study on ant-based clustering algorithms is described. We also present some applications of ant-based clustering algorithms.
AN EFFICIENT CLUSTERING ALGORITHM FOR OUTLIER DETECTION
(International Journal of Computer Applications, 2011-10) Vijayarani S; Nithya S
With the help of data mining, an important and valuable knowledge is extracted from the large massive collection of data. There are s several techniques and algorithms are used for extracting the hidden patterns from the large data sets and finding the relationships between them. Clustering is one of the important techniques in data mining. Clustering algorithms are used for grouping the data items based on their similarity. Outlier Detection is a very important research problem in data mining. Clustering algorithms are used for detecting the outliers efficiently. In this research paper, we focused on outlier detection in health data sets such as Pima Indians Diabetes data set and Breast Cancer Wisconsin data set using partitioning clustering algorithms. The algorithms used in this research work are PAM, CLARA AND CLARANS and a new clustering algorithm ECLARANS is proposed for detecting outliers. In order to find the best clustering algorithm for outlier detection several performance measures are used. The experimental results show that the outlier detection accuracy is very good in the proposed ECLARANS clustering algorithm compared to the existing algorithms.
SENSITIVE OUTLIER PROTECTION IN PRIVACY PRESERVING DATA MINING
(International Journal of Computer Applications, 2011-11) Vijayarani S; Nithya S
Data mining is the extraction of hidden predictive information from large databases and also a powerful new technology with great potential to analyze important information in their data warehouses. Privacy preserving data mining is a latest research area in the field of data mining which generally deals with the side effects of the data mining techniques. Privacy is defined as “protecting individual’s information”. Protection of privacy has become an important issue in data mining research. Sensitive outlier protection is novel research in the data mining research field. Clustering is a division of data into groups of similar objects. One of the main tasks in data mining research is Outlier Detection. In data mining, clustering algorithms are used for detecting the outliers efficiently. In this paper we have used four clustering algorithms to detect outliers and also proposed a new privacy technique GAUSSIAN PERTURBATION RANDOM METHOD to protect the sensitive outliers in health data sets.
PANCREATIC TUMOR SEGMENTATION BASED ONOPTIMIZED K-MEANS CLUSTERING AND SALIENCYMAP MODEL
(lnternational Journal of Recent Technology and Engineering(IJRTE), 2019-11) Sindhu A; Radha V
Segmentation of positron emission tomography (PET) plays a major role in research and clinical applications. The segmentation of pancreatic tumors using PET / CT is challenging due to a significant amount of noise that may result in serious segmentation inaccuracies. The evaluation of the results of segmentation in medical imaging is due to the presence of a gold standard. Therefore, the performance evaluation of these methods would be necessary. This paper suggested a new object segmentation method that is based on K-means clustering with Saliency Maps. The K-means clustering approach restricts every pixel of the image that belongs to a single cluster. One drawback with using the K-means algorithm to segment objects is that segments are not connected and can be widely scattered. It is known that using saliency region, the approximate location of the desired object in the map can easily be identified. In this proposed method, the saliency map is used to distinguish the desired object cluster from the image from the background cluster, and then, to map the object clusters together Experimental results shows that the proposed algorithm outperforms dramatically in terms of visual plausibility and computational cost compared to state-of - the-art methods and achieves excellent performance for object segmentation.

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