Clustering Methods

Data, which can be defined as an entity of meaning, it is the original material to construct message and knowledge. Thanks to the assistance of computer, data processing

Data, Data everywhere. It is a precious thing that will last longer than the systems. In this challenging world, there is a high demand to work efficiently without risk of losing any tiny information which might be very important in future. Hence there is need to create large volumes of data which needs to be stored and explored for future analysis. I am always fascinated to know how this large amount of data is handled, stored in databases and manipulated to extract useful information. A raw data is like an unpolished diamond, its value is known only after it is polished. Similarly, the value of data is understood only after a proper meaning is brought out of it, this is known as Data Mining.

Frequent itemsets play an main role in a lot of data mining tasks that try to get interesting patterns in databases, such as association rules, clusters, sequences correlations, episodes and classier. Although the number of all frequent itemsets is usually very large, the subset that is really interesting for the user typically contains only a small number of itemsets. Therefore, the model of constraint-based mining was introduced. Constraints provide focus on the interesting knowledge, thus decrease the number of patterns extracted to those of possibility interest. Additionally, they can be

Data mining is the process through which previously unknown patterns in data were discovered. Another definition would be “a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases.” This includes most types of automated data analysis. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models.

Nowadays, data mining and machine learning become rapidly growing topics in both industry and academic areas. Companies, government laborites and top universities are all contributing in knowledge discovery of pattern recognition, text categorization, data clustering, classification prediction and more. In general, data mining is the technique used to analyze data from multi perspectives and reveal the hidden gem behind the enormous amount of data. With the explosive growth of data collections, it becomes time-consuming less effective to extract valuable information from massive databases through the use of traditional data analysis methods. An alternative way to solve this problem is to apply data mining, given considerations

Clustering is a fundamental approach in data mining and its aim is to organize data into distinct groups to identify intrinsic hidden patterns of data. In other words, clustering methods divide a set of instances into several groups without any prior knowledge using the similarity of objects in which patterns in the same group have more similarities to each other than patterns in different groups. It has been successfully applied in various fields such as image processing (Wu & Leahy, 1993) cybersecurity (Kozma, Rosa, & Piazentin, 2013), pattern recognition (Haghtalab, Xanthopoulos, & Madani, 2015), bioinformatics(C. Xu & Su, 2015), protein analysis (de Andrades, Dorn, Farenzena, & Lamb, 2013), microarray analysis (Castellanos-Garzón,

Some important clustering algorithms discussed in this paper to group massive data and can be useful to industries and organization:

Data mining prediction model works on the process of identifying the patterns based on the historical information to predict the new incoming data sets. This prediction modelling is much useful in the case of decision making process in the business models. On the other way, Descriptive model describes the data in an efficient way by means of grouping the data by using clustering; association rules principles of data mining.

Association rule mining was invented to extract patterns from transactional databases. As stated, an association rule is an method applied in the form X →Y, where X and Y are sets of items. Association rule mining finds all such conditions which

Data mining is the process of extracting useful knowledge from large databases or data warehouses. It can be also said as a set of mathematical functions and data manipulation techniques to extract useful data from databases. Data mining can also be said as knowledge discovery process in other words. It explores a large collection of data into a meaningful patterns and rules based on the queries provided by users using data mining query language. The meaningful patterns and rules are generated by analysing the database. Data mining makes use several techniques such as clustering, classification, association rule mining and so on to generate the meaningful patterns from the databases. The purpose of this report is to describe how data are prepared for data

Abstract— Data mining is the method of extracting the data from large database. Various data mining techniques are clustering, 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 for Knowledge Analysis) for analyzing the clustering techniques. Here the time, Clustered and un-clustered

The Enhanced K-Strange Points Clustering algorithm converged faster with less number of steps than the K-Means Clustering algorithm.

The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori. The main idea is to define k centers, one for each cluster. These centers should be placed in a cunning way because of different location causes different result. So, the better choice is to place them as much as possible far away from each other. The next step is to take each point belonging to a given data set and associate it to the nearest center. When no point is pending, the first step is completed and an early group age is done. At this point we need to re-calculate k new centroids as barycenter of the clusters resulting from the previous step. After we have these k new centroids, a new binding has to be done between the same data set points and the nearest new center. A loop has been generated. As a result of this loop we may notice that the k centers change their location step by step until no more changes are done or in other words centers do not move any more. Finally, this algorithm aims at minimizing

2 Assistant Professor, Dept. Of Computer Science & Engineering, CT Institute of Technology & Research, Jalandhar, Punjab 144008, India

Data is specific and it is organized for a purpose accurately and timely. Presented within a context, it gives its meaning and relevance. The uncertainty also gets minimized.