3,636 research outputs found
A Comparative Agglomerative Hierarchical Clustering Method to Cluster Implemented Course
There are many clustering methods, such as hierarchical clustering method.
Most of the approaches to the clustering of variables encountered in the
literature are of hierarchical type. The great majority of hierarchical
approaches to the clustering of variables are of agglomerative nature. The
agglomerative hierarchical approach to clustering starts with each observation
as its own cluster and then continually groups the observations into
increasingly larger groups. Higher Learning Institution (HLI) provides training
to introduce final-year students to the real working environment. In this
research will use Euclidean single linkage and complete linkage. MATLAB and HCE
3.5 software will used to train data and cluster course implemented during
industrial training. This study indicates that different method will create a
different number of clusters.Comment: 6 pages, 10 figures, published on Journal of Computing, Volume 2,
Issue 12, December 201
Statistical Mechanics of Community Detection
Starting from a general \textit{ansatz}, we show how community detection can
be interpreted as finding the ground state of an infinite range spin glass. Our
approach applies to weighted and directed networks alike. It contains the
\textit{at hoc} introduced quality function from \cite{ReichardtPRL} and the
modularity as defined by Newman and Girvan \cite{Girvan03} as special
cases. The community structure of the network is interpreted as the spin
configuration that minimizes the energy of the spin glass with the spin states
being the community indices. We elucidate the properties of the ground state
configuration to give a concise definition of communities as cohesive subgroups
in networks that is adaptive to the specific class of network under study.
Further we show, how hierarchies and overlap in the community structure can be
detected. Computationally effective local update rules for optimization
procedures to find the ground state are given. We show how the \textit{ansatz}
may be used to discover the community around a given node without detecting all
communities in the full network and we give benchmarks for the performance of
this extension. Finally, we give expectation values for the modularity of
random graphs, which can be used in the assessment of statistical significance
of community structure
An exploration of methodologies to improve semi-supervised hierarchical clustering with knowledge-based constraints
Clustering algorithms with constraints (also known as semi-supervised clustering algorithms) have been introduced to the field of machine learning as a significant variant to the conventional unsupervised clustering learning algorithms. They have been demonstrated to achieve better performance due to integrating prior knowledge during the clustering process, that enables uncovering relevant useful information from the data being clustered. However, the research conducted within the context of developing semi-supervised hierarchical clustering techniques are still an open and active investigation area. Majority of current semi-supervised clustering algorithms are developed as partitional clustering (PC) methods and only few research efforts have been made on developing semi-supervised hierarchical clustering methods. The aim of this research is to enhance hierarchical clustering (HC) algorithms based on prior knowledge, by adopting novel methodologies. [Continues.
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The role of human factors in stereotyping behavior and perception of digital library users: A robust clustering approach
To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception
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