Cluster analysis

classification, association analysis, regression, summarization, time series analysis and sequence analysis, etc. Clustering is one of the important tasks in mining and is said to be unsupervised classification. Clustering is the techniques which is used to group similar objects or processes. In this work four clustering algorithms (K-Means, Farthest first, EM, Hierarchal) have been analyzed to cluster the data and to find the outliers based on the number of clusters. Here the WEKA (Waikato Environment

Abstract—A tumor is the growth in the abnormal tissue of the brain which causes damage to the other cells necessary for functioning. Detection of brain tumor is a difficult task, as there are various techniques involved in it. The active imaging resource used for brain tumor detection is Magnetic Resonance Imaging (MRI). It is necessary to use technique which can give the accurate location and size of the tumor. There are various algorithms proposed for brain tumor detection, this paper presents

Ganapathy Engineering College , Hunter Raod ,Warangal Mr.M.Rajesh Assistant Professor, Department of CSE Ganapathy Engineering College , Hunter Raod ,Warangal Abstract— This All clustering methods have to assume some cluster relationship among the data objects that they are applied on. Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel multi-viewpoint based similarity measure and two related clustering

MASTER OF COMPUTER and INFORMATION SCIENCES COMP 809 Data Mining & Machine Learning ASSIGNMENT ONE Semester 1, 2015 PART ‘A’ CASE STUDY FOR NEEDY STUDENTS IN A UNIVERSITY USING RFM MODEL BASED ON DATA MINING.(Bin, Peiji, & Dan, 2008)  ABSTRACT: Provision of education for each & every student should be the basic initiative for the government in colleges & universities. For higher education many students are short of their tuition fees with popularization of their educational course. In

Specification Operating System K-Strange 2 clusters (Sec) K-Strange 3 clusters (Sec) K-means 2 clusters (Sec) K-means Clusters (Sec) Intel(R) Core (TM) i5-4210U CPU @ 1.70 GHz 2.40 GHz RAM:- 8.00 GB Windows 64-bit Operating System, x64-based processor. 0.09 0.122 0.098 0.185 Intel(R) Core (TM) i3-4130U CPU @ 3.40 GHz 3.40 GHz RAM:- 4.00 GB Windows 64-bit Operating System, x64-based processor. 0.08 0.156 0.086 0.096 2.7 GHZ Dual Core Intel Core i5 RAM: 8 GB Mac OS (10.12) sierra 0.04

“ Image Segmentation Of Detection Of Lump Using Algorithm” Nikhil B Bhosle Bhagban J Choudhury Nilesh S Magam Project Guide:-J.P.Patil (bhosle.nikhila03@gmail.com) (bhagbanchoudhury18@gmail.com) (nil25may@gmail.com) (jeetoo.patil@gmail.com) Abstract— Tumor is a swelling of a part of the body, generally without inflammation, caused by an abnormal growth of cells it is also known as cancerous growth

mining, and information filtering. So, the cluster technology is becoming the core of text mining. Clustering is an important form of data mining. Clustering is a process of grouping similar sets of data into a group, called clusters. This paper comprises of text clustering algorithms, also analysis and comparison of the algorithms are done with respect to the applicable scope, the initial parameters , size of dataset, accuracy, dimensionality, cluster shape and noise sensitivity. Algorithms are

time, such eco-regions require to be managed with special emphasis and consideration of its natural and cultural resources. Land cover classification of such diverse sea-land regions requires a high level of effort and plays a vital role for the analysis of time-based or event-based change on certain areas. For effective utilization of remote sensed images obtained from Earth-orbiting satellites, a lot of image classification methods are available in literature. In this paper, we use the method of

Many other terms are being used to interpret data mining, such as knowledge mining from databases, knowledge extraction, data analysis, and data archaeology. Data mining is one of the provoking and significant areas of research. Data mining is implicit and non-trivial task of identifying the viable, novel, inherently efficient and perspicuous patterns of data. Figure 1 represents

clustering is the identification of clusters in given data. A widely used method for clustering is based on K-means in which the data is partitioned into K number of clusters. In this method, clusters are predefined which is highly dependent on the initial identification of elements representing the clusters well. Several researchers in clustering has focused on improving the clustering process such that the clusters are not dependent on the initial identification of cluster representation. keywords : Segmentation