Now a day’s digital world data in computers has great significance and this data is extremely critical in perspective for upcoming position and learn irrespective of different fields. Therefore we assessment of such data is vital and imperative task. Computer forensic analysis a lot of data there in the digital campaign is study to extract data and computers consist of hundreds of thousands of ...

A problem with the modelling of clusters as d dimensional centroids is that centroids cannot relay much information about cluster shape i.e. elongated, circular, irregular etc... The Agglomerative-Partitional Clustering (APC) methodology introduced here attempts to remedy this situation by joining together centroids coexisting within regions of relatively high density with line segments. Interc...

Extensive amount of data stored in medical documents require developing methods that help users to find what they are looking for effectively by organizing large amounts of information into a small number of meaningful clusters. The produced clusters contain groups of objects which are more similar to each other than to the members of any other group. Thus, the aim of high-quality document clus...

Author co-citation analysis (ACA) has been widely used in bibliometrics as an analytical method in analyzing the intellectual structure of science studies. It can be used to identify authors from the same or similar research fields. However, such analysis method relies heavily on statistical tools to perform the analysis and requires human interpretation. Web Citation Database is a data warehou...

Cluster analysis is a common task in data mining, machine learning and related fields. There exist a plethora of clustering algorithms designed for this purpose, but many are prohibitively inefficient (e.g. quality-threshold clustering), non-deterministic (k-means) or utilise inherently lossy partitioning models (k-d tree clustering). Single-linkage hierarchical clustering is a form of cluster ...

Hierarchical clustering constructs a hierarchy of clusters by either repeatedly merging two smaller clusters into a larger one or splitting a larger cluster into smaller ones. The crucial step is how to best select the next cluster(s) to split or merge. Here we provide a comprehensive analysis of selection methods and propose several new methods. We perform extensive clustering experiments to t...