Machine learning and data mining methods are applied to perform large data analysis. Clustering methods are applied to group the related data values. Partitional clustering and hierarchical clustering methods are applied to handle the clustering operations. Tabular format data processing is carried out under the partitional clustering models. Tree based data clustering is adapted in the hierarc...

Network monitoring applications are used to monitor network traffic flows. Clustering techniques are used to extract network traffic patterns. Anomaly detection schemes are used to detect network attacks. Hierarchical and partitional clustering schemes are used to analyze network traffic data values. The hierarchical data analysis uses the structure and data values for the clustering process. T...

Abstract— partitioning a large set of objects into homogeneous clusters is a fundamental operation in data mining. The operation is needed in a number of data mining tasks such as unsupervised classification and data summation as well as segmentation of large heterogeneous data sets into smaller homogeneous subsets that can be easily managed, separately modeled and analyzed. Clustering is a pop...

Clustering of text documents is an important technique for documents retrieval. It aims to organize documents into meaningful groups or clusters. Preprocessing text plays a main role in enhancing clustering process of Arabic documents. This research examines and compares text preprocessing techniques in Arabic document clustering. It also studies effectiveness of text preprocessing techniques: ...

We are propose a hybrid clustering method, the methodology combines the strengths of both partitioning and agglomerative clustering methods. Clustering algorithms that build meaningful hierarchies out of large document collections are ideal tools for their interactive visualization and exploration as they provide data-views that are consistent, predictable, and at different levels of granularit...

The innumerable clustering methods which exist today form the basis of Data Mining and Cluster Analysis. This paper details the distinct classifications of clustering methods, describes prominent examples for each such classification and aims to bring about the comparison between the primary clustering techniques which form the basis of all the others, i.e. the Hierarchical and Partitional algo...

Considered many partitional clustering algorithms are originated from the definition of mean, we propose a new clustering model---general c-means clustering algorithm (GCM). Generally, when the data set is clustered into c (c>1) subsets, each subset is often expected to have a different prototype (or cluster center) than others. Therefore, we propose the definition of undesirable solution of cl...

There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web. Fast and high-quality document clustering algorithms play an important role in helping users to effectively navigate, summarize and organize the information. Recent studies have shown that partitional clustering algorithms are more suitable for clustering larg...