Data Mining, Partition Based Clustering

Usually the data mining analysis is done by grouping commonly co-occuring things (Associations), discovering time-ordered events (Sequences), anticipating future occurences (Predictions), identifying natural groupings of items (Clusters) and finally, by uncovering generalizations to help classify items (Classification). These different type of mining usually take a lot of time and a good understanding of the business and

Today with the ever growing use of computers in the world, information is constantly moving from one place to another. What is this information, who is it about, and who is using it will be discussed in the following paper. The collecting, interpreting, and determination of use of this information has come to be known as data mining. This term known as data mining has been around only for a short time but the actual collection of data has been happening for centuries. The following paragraph will give a brief description of this history of data collection.

In its infancy, data mining was as limited as the hardware being used. Large amounts of data were difficult to analyze because the hardware simply could not handle it [1]. The term "data mining" first began appearing in the 1980 's largely within the research and computer science communities. In the 1990 's it was considered a subset of a process called Knowledge Discovery in Databases of KKD [1]. KKD analyzes data in the search for patterns that may not normally be recognized with the naked eye. Today however, data mining does not limit itself to databases,

As probably the most natural type of storing information is text and text mining is believed to have a commercial potential greater than that of data mining. The recent study indicated that 80% of a company’s information is including in text documents. Text mining, however, is also a more complex task as compare to data mining as it involves dealing with text data that are naturally unstructured and fuzzy. Text mining is a multidisciplinary area, involving information retrieval, text examination, information extraction, clustering, categorization, visualization, database technology, device learning, and data mining [2]. In Text Mining, patterns are extracted from natural language Textual Database. There are many methods of text mining. In general, the major approaches, based on the kinds of data they take as input,

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

A data stream is a real time, continuous, structured sequence of data items. Mining data stream is the process of extracting knowledge from continuous, rapid data records. Data arrives faster, so it is a very difficult task to mine that data. Stream mining algorithms typically need to be designed so that the algorithm works with one pass of the data. Data streams are a computational challenge to data mining problems because of the additional algorithmic constraints created by the large volume of data. In addition, the problem of temporal locality leads to a number of unique mining challenges in the data stream case. The data mining techniques namely clustering, classification and frequent pattern mining are applied to extract the knowledge

Big data analytics methods and techniques (which is the application of predictive methods, pattern recognition techniques, cluster analysis, and other quantitative and qualitative methods in big data sets) can

Background - One of the most promising developments in the field of computing and computer memory over the past few decades has been the ability to bring tremendous complex and large data sets into database management that are both affordable and workable for many organizations. Improvement in computer power has also allowed for the field of artificial intelligence to evolve which also improves the sifting of massive amounts of information for appropriate use in business, military, governmental, and academic venues. Essentially, data mining is taking as much information as possible for a variety of databases, sifting it intelligently and coming up with usable information that will help with data prediction, customer service, what if scenarios, and extrapolating trends for population groups (Ye, 2003; Therling, 2009).

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 methods. The major difference between a traditional dissimilarity/similarity measure and ours is that the former uses only a single viewpoint, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are proposed based on this new measure. We compare them with several well-known clustering algorithms that use other popular similarity measures on various document collections to verify the advantages of our proposal.

2. Detection using factors: The pre- duplicate record elimination stage is useful for removing data but it helps in retaining only one copy of the duplicate data and removing the rest. For this purpose, a threshold value is calculated for all the records and a similarity. Threshold value is calculated for elimination purpose. All the possible pairs are selected from the clusters

Data mining techniques are basically categorised into two major groups as Supervised learning and Unsupervised learning. Clustering is a process of grouping the similar data sets into groups. These groups should have two properties like dissimilarity between the groups and similarity within the group. Clustering is covered in the unsupervised learning category. There are no predefined class label

The exponential growth of the data may lead us to a time in future where huge amount of data would not be able to be managed easily. Text Classification is done through Text Mining study which would help sorting the important texts from the content or a document to manage the data or information easily.

Clustering is practicable in various explorative pattern-analysis, grouping, decision-making, and machine learning circumstances, including data mining, document retrieval, image segmentation, and pattern classification [1]. Clustering a set of

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].

Abstract— The set of objects having same characteristics are organized in groups and clusters of these objects are formed known as Data Clustering.It is an unsupervised learning technique for classification of data. K-means algorithm is widely used and famous algorithm for analysis of clusters.In this algorithm, n number of data points are divided into k clusters based on some similarity measurement criterion. K-Means Algorithm has fast speed and thus is used commonly clustering algorithm. Vector quantization,cluster analysis,feature learning are some of the application of K-Means.However results generated using this algorithm are mainly dependant on choosing initial cluster centroids.The main shortcome of this algorithm is to provide appropriate number of clusters.Provision of number of clusters before applying the algorithm is highly impractical and requires deep knowledge of clustering