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Articles 31 - 37 of 37
Full-Text Articles in Databases and Information Systems
The Social Network Of Software Engineering Research, Subhajit Datta, Nishant Kumar, Santonu Sarkar
The Social Network Of Software Engineering Research, Subhajit Datta, Nishant Kumar, Santonu Sarkar
Research Collection School Of Computing and Information Systems
The social network perspective has served as a useful framework for studying scientific research collaboration in different disciplines. Although collaboration in computer science research has received some attention, software engineering research collaboration has remained unexplored to a large extent. In this paper, we examine the collaboration networks based on co-authorship information of papers from ten software engineering publication venues over the 1976-2010 time period. We compare time variations of certain parameters of these networks with corresponding parameters of collaboration networks from other disciplines. We also explore whether software engineering collaboration networks manifest symptoms of the small-world phenomenon, conform to the …
Efficient Clustering Techniques For Managing Large Datasets, Vasanth Nemala
Efficient Clustering Techniques For Managing Large Datasets, Vasanth Nemala
UNLV Theses, Dissertations, Professional Papers, and Capstones
The result set produced by a search engine in response to the user query is very large. It is typically the responsibility of the user to browse the result set to identify relevant documents. Many tools have been developed to assist the user to identify the most relevant documents. One such a tool is clustering technique. In this method, the closely related documents are grouped based on their contents. Hence if a document turns out to be relevant, so are the rest of the documents in the cluster. So it would be easy for a user to sift through the …
Multi-Order Neurons For Evolutionary Higher Order Clustering And Growth, Kiruthika Ramanathan, Sheng Uei Guan
Multi-Order Neurons For Evolutionary Higher Order Clustering And Growth, Kiruthika Ramanathan, Sheng Uei Guan
Research Collection School Of Computing and Information Systems
This letter proposes to use multiorder neurons for clustering irregularly shaped data arrangements. Multiorder neurons are an evolutionary extension of the use of higher-order neurons in clustering. Higher-order neurons parametrically model complex neuron shapes by replacing the classic synaptic weight by higher-order tensors. The multiorder neuron goes one step further and eliminates two problems associated with higher-order neurons. First, it uses evolutionary algorithms to select the best neuron order for a given problem. Second, it obtains more information about the underlying data distribution by identifying the correct order for a given cluster of patterns. Empirically we observed that when the …
Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao
Automatically Discovering The Number Of Clusters In Web Page Datasets, Zhongmei Yao
Computer Science Faculty Publications
Clustering is well-suited for Web mining by automatically organizing Web pages into categories, each of which contains Web pages having similar contents. However, one problem in clustering is the lack of general methods to automatically determine the number of categories or clusters. For the Web domain in particular, currently there is no such method suitable for Web page clustering. In an attempt to address this problem, we discover a constant factor that characterizes the Web domain, based on which we propose a new method for automatically determining the number of clusters in Web page data sets. We discover that the …
The Edam Project: Mining Atmospheric Aerosol Datasets, Raghu Ramakrishnan, James J. Schauer, Lei Chen, Zheng Huang, Martin M. Shafer, Deborah S. Gross, David R. Musicant
The Edam Project: Mining Atmospheric Aerosol Datasets, Raghu Ramakrishnan, James J. Schauer, Lei Chen, Zheng Huang, Martin M. Shafer, Deborah S. Gross, David R. Musicant
Chemistry Faculty Work
Data mining has been a very active area of research in the database, machine learning, and mathematical programming communities in recent years. EDAM (Exploratory Data Analysis and Management) is a joint project between researchers in Atmospheric Chemistry and Computer Science at Carleton College and the University of Wisconsin-Madison that aims to develop data mining techniques for advancing the state of the art in analyzing atmospheric aerosol datasets. There is a great need to better understand the sources, dynamics, and compositions of atmospheric aerosols. The traditional approach for particle measurement, which is the collection of bulk samples of particulates on filters, …
Towards Personalised Web Intelligence, Ah-Hwee Tan, Hwee-Leng Ong, Hong Pan, Jamie Ng, Qiu-Xiang Li
Towards Personalised Web Intelligence, Ah-Hwee Tan, Hwee-Leng Ong, Hong Pan, Jamie Ng, Qiu-Xiang Li
Research Collection School Of Computing and Information Systems
The Flexible Organizer for Competitive Intelligence (FOCI) is a personalised web intelligence system that provides an integrated platform for gathering, organising, tracking, and disseminating competitive information on the web. FOCI builds personalised information portfolios through a novel method called User-Configurable Clustering, which allows a user to personalise his/her portfolios in terms of the content as well as the organisational structure. This paper outlines the key challenges we face in personalised information management and gives a detailed account of FOCI’s underlying personalisation mechanism. For a quantitative evaluation of the system’s performance, we propose a set of performance indices based on information …
Modified Art 2a Growing Network Capable Of Generating A Fixed Number Of Nodes, Ji He, Ah-Hwee Tan, Chew-Lim Tan
Modified Art 2a Growing Network Capable Of Generating A Fixed Number Of Nodes, Ji He, Ah-Hwee Tan, Chew-Lim Tan
Research Collection School Of Computing and Information Systems
This paper introduces the Adaptive Resonance Theory under Constraint (ART-C 2A) learning paradigm based on ART 2A, which is capable of generating a user-defined number of recognition nodes through online estimation of an appropriate vigilance threshold. Empirical experiments compare the cluster validity and the learning efficiency of ART-C 2A with those of ART 2A, as well as three closely related clustering methods, namely online K-Means, batch K-Means, and SOM, in a quantitative manner. Besides retaining the online cluster creation capability of ART 2A, ART-C 2A gives the alternative clustering solution, which allows a direct control on the number of output …