The Clustering Is A Data Mining Technique

Also, it is possible that the k-means algorithm won't find a final solution.  In this case it would be a good idea to consider stopping the algorithm after a pre-chosen maximum of

1. Based only on the cluster analysis data, which preference related variables are most useful for segment identification and evaluation? Which variables are least useful?

This study utilized the Worchester Heart Attack Study data and R Studio software to predict the mortality factors for heart attack patients. The medical data include physiological measurements about heart attack patients, which serve as the independent variables, such as the heart rate, blood pressure, atria fibrillation, body mass index, cardiovascular history, and other medical signs. This study employed the techniques of supervised learning and unsupervised learning algorithms, using classification decision trees and k-means clustering, respectively. In addition to performing initial descriptive statistics to estimate the general range of critical factors correlated with heart attack patients, R Studio was used to determine the weight of each of the significant factors on the prediction in order to quantify its influence on the death of heart attack patients. Furthermore, the software was used to evaluate the accuracy of the predicted model to estimate death of heart attack patients by using a confusion matrix to compare predictions with actual data. Finally, this study reflected on the effectiveness of the data mining software conclusions, compared supervised learning and unsupervised learning, and conjectured improvements for future data mining investigations.

This technique is based on the popular k-means clustering algorithm. The clustering algorithm can work on data with many dimensions and target to reduce the distance within clusters. In the same time, it increases the distance between clusters. Initiating with K centers, the method iteratively assign each point to its nearest center dependent on

The K-means algorithm is an unsupervised clustering algorithm which partitions a set of data, usually termed dataset into a certain number of clusters. Minimization of a performance index is the primary basis of K-means Algorithm, which is defined as the sum of the squared distances from all points in a cluster domain in the cluster center. Initially K random cluster centers are chosen. Then, each sample in the sample space is assigned to a cluster based on the minimum distance to the center of the cluster. Finally the cluster centers are updated to the average of the values in each cluster. This is repeated until cluster centers no longer change. Steps in the K-means algorithm are [K.M. Murugesan and S.

ACHTH –LEACH: The author has instigated ACHTH - LEACH to enhance LEACH and rectify its defects. The clusters are set up in light of the Greedy k- means algorithm. The cluster heads are chosen by considering the lingering vitality of sensor nodes. Furthermore, the bunch heads may embrace two-top transmission to lessen the vitality spent on sending data to the BS.

All the aforementioned methods are highly capable of spherical clustering, but fail to deliver an appropriate performance for non-spherical data [26]. To solve this problem, the fuzzy-based clustering kernel is used to map the data points to Hilbert high-dimensional space through the kernel functions. Yang and Tsai [27] proposed an FCM variant based on a Gaussian kernel (GKFCM). In their method, a parameter named is calculated in each iteration and replaced in the cluster. As for FCM-S1 and FCM-S2, this method can also be applied to forms GFKCM1 and GKFCM2, which use the mean and median filters, respectively. In this method, is estimated using the kernel functions. Replacing with can improve the results compared to those of FCM-S1 and FCM-S2. Regardless, an appropriate estimation of requires the cluster centers to bewell-separated, which is not always possible. Hence, the algorithm should run a large number of iterations to achieve convergence.

Grouping similar customers and products has been used prominently in market segmentation and this is also the fundamental in marketing activity (E.Mooi and M.Sarstedt, 2011). This method is known as the cluster analysis and it is a multivariate method which classifies a sample

Data clustering is a method used to group items into different clusters. The items in same cluster are similar and the items in different clusters are dissimilar. Huang (1998) introduced a k-prototypes algorithm that allows for clustering objects with mixed numeric and categorical attributes. The k-prototype algorithm can be used to cluster a large portfolio of the VA contracts with mixed numeric and categorical attributes.

When all the items of the data set are assigned to one of the centroid the first stage is completed and an early set of clusters is obtained. After the first stage we recalculate to find the new centroids and then again find the distances between the data set entities and the centroids. The same process is iterated till the centroids become stable and there are no more changes in it. The K-Means algorithm is fast robust and easier to understand compared to the other clustering algorithms. Also it provides better results when the data items are well separated or distinct from each other.

Fuzzy clustering plays an important role in feature analysis, system identification and classifier design [21]. Given its capacity for managing uncertainty, impreciseness and vagueness, the fuzzy algorithm is far more realistic for solving real-world problems than hard clustering algorithms. The fuzzy C-mean (FCM) clustering algorithm [22] is a well-known method based on fuzzy clustering. In this method, the image is represented in different feature spaces and the FCM classifies similar data points according to the distance of the pixel from the center of the feature space.

For measuring the quality of clusters four criteria have been used. The first three criteria are designed so as to measure the quality of cluster sets at different levels of granularity. Ideally it’s needed to generate partitions that have compact, well separated clusters. Hence, the criteria used presently combine the two measures to return a value that indicates the quality of the partition thus the value returned is minimized when the partition is judged to consist of compact well separated clusters with different criteria judging different partition as the best one. The last criterion is based on time efficiency.

Data Mining is the non-trivial extraction of potentially useful information about data. In other words, Data Mining extracts the knowledge or interesting information from large set of structured data that are from different sources. There are various research domains in data mining specifically text mining, web mining, image mining, sequence mining, process mining, graph mining, etc. Data mining applications are used in a range of areas such as it is used for financial data analysis, retail and telecommunication industries, banking, health care and medicine. In health care, the data mining is mainly used for disease prediction. In data mining, there are several techniques have been developed and used for predicting the diseases that includes data preprocessing, classification, clustering, association rules and sequential patterns. This paper analyses the performance of two classification techniques such as Bayesian and Lazy classifiers for hepatitis dataset. In Bayesian classifier there are two algorithms namely BayesNet and NaiveBayes. In Lazy classifier we have two algorithms namely IBK and KStar. Comparative analysis is done by using the WEKA tool.It is open source software which consists of the collection of machine learning algorithms for data mining tasks.

Also, they require a threshold to define an appropriate stopping condition for splitting or merging of partitions (Johnson, 1967). Although, they have several advantages such as a better visualization of clusters by generating a tree without predefining the number of clusters but, calculating and sorting the Euclidean distances require a high computational and memory costs. On the other hand, grid-based algorithms have a high efficiency and time complexity is independent of the number of data objects (Yue, Wang, Tao, & Wang, 2010).

The geographic information system is also used in finding clusters. This is done using multiple algorithms to come with a group of unrelated regions that match the theme of interest. The cluster contains points that meet the criterion required for the theme. For instance, members of a cluster could be points where the distance between them is less than a particular threshold or points whose population density is above a particular range. The process requires many levels of iteration before the choice of the correct algorithm can be identified. Cluster identification has been used in different organizations to group oil deposits depending on their size based on the population surrounding them. There are several techniques and models used in