Comparison On Various Clustering Algorithms

Pearson, Griffins, Biddle, Johnson, McGeorge, and Haycraft (2017) conducted an experiment about how screen-time and eating behaviour are related among young adolescents. Adolescence is considered as a healthy stage of life, however; young people can form unhealthy eating behaviour at this age. Low intake of fruit and vegetable, and high intake of energy-dense snack foods are some of unhealthy eating behaviour are commonly established and developed from this life stage and can persist into adulthood. Individual adolescent health behaviours are concerned as independent behaviours, it is still important to look at the correlation of clustering of unhealthy behaviours such as screen-time and low vegetable consuming. Forming unhealthy eating behavior

Abstract - In the Data mining process, we can identify the patterns in the data that is hard to find using normal analysis. Several Mathematical and statistical algorithms are used in this approach to determine the probability of the event or scenario. The main aim of this process in terms of technical representation is to find the correlation amongst the attributes. There is a huge amount of discovery being carried out in this field creating a huge scope and jobs in this area. Several data mining algorithms are present that could determine different features present in the data that could lead in prediction and future analysis. Main Study report would consist of these algorithms that could help us predict and some sample data that we

1.3 Literature Review on Seven Data Mining Techniques . . . . . . . . . . . . . . . . . 6

Data mining is the extraction of knowledge from the various databases that was previously unknown (Musan & Hunyadi, 2010). Data mining consists of using software that conglomerates artificial intelligence, statistical analysis, and systems management in the act of extracting facts and understanding from data stored in data warehouses, data marts, and through metadata (Giudici, 2005). Through algorithms and learning capabilities data mining software can analyze large amounts of data and give the management team intellectual and effective information to help them form their decisions. The intention for data mining is to analyze prevailing data and form new truths and new associations that were unknown prior to the analysis (Musan & Hunyadi,

Data mining is the procedure of getting new patterns from large amount of data. Data mining is a procedure of finding of beneficial information and patterns from huge data. It is also called as knowledge discovery method, knowledge mining from data, knowledge extraction or data/ pattern analysis. The main goal from data mining is to get patterns that were already unknown. The useful of these patterns are found they can be used to make certain decisions for development of their businesses. Data mining aims to discover implicit, already unknown, and potentially useful information that is embedded in data.

Digital imaging technologies have become indispensable components for clinical procedures. Major advances in the field of medical imaging and computer technology have created opportunity for quantitative analyses of medical images and provided powerful techniques to probe the structure, pathology and function of the human body . In medical applications, skilled operators usually extract the desired regions that may be anatomically separate but statistically indistinguishable. It is subjected to manual errors and biases, which is time consuming, and has poor reproducibility. The problem faced in 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.

This research paper is about the Comparative analysis of three data mining software’s selected based on four important criteria Performance, Functionality, Usability and Ancillary Tasks support. “Data Mining is a field of study that is gaining importance and is used to explore data in search of patterns or relationships between variables and is applied to new data used for predictions”. (Statistics – Textbook. (n.d.). Retrieved November 17, 2015). Selection of the appropriate data mining tools is critical to any research or business and this could impact the business in terms of money, resources and time. Data experts

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).

it is the most effective data mining technique to discover hidden or desired pattern among the large amount of data. It is responsible to find correlation relationships among different data attributes in a large set of items in a database .

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.

First of all, a new idea of using PSO for k-means clustering technique is presented by Vander Merwe and Engelbrecht. Different from traditional k-means clustering, which starts with multiple solutions to the problem, the new approach specifies a fixed number of particles as a swarm, each of which representing the centroids of all clusters. A new set of better solutions is generated after a successive iterations based on previous solutions. As the first proposed method using PSO based on clustering, it does not improve the efficiency of execution time, while this method provides a new way to optimize clustering.

Traditionally Data Mining is a process of extracting useful knowledge from a large volume of data set. The generated knowledge is applied and used for the most of the applications in all the areas such as science, engineering, business, research, social, health, education, entertainment and all. As all the

[1] Jaiwei Han and Micheline Kamber “Data Mining: Concepts and Techniques”,Morgan Kaufmann Publications Second Edition,2006

2. Classification Tree Analysis: Statistical classification is a method of identifying categories that a new observation belongs to. It requires a training set of correctly identified observations – historical data in other words.

The proposed model performs the segmentation process using clustering method which is considered an efficient segmentation technique. Clustering is the process of classifies objects into different groups and classes. In clustering the data is classified according to a common features shared between the object and the target class. The process of portioning data into a number of subsets is often referred to as supervised learning or clustering. K-means is one of clustering techniques designed and used in many approaches [4].