@article {236,
	title = {Extraction of children{\textquoteright}s friend relations from activity level},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Export Date: 10 July 2014},
	month = {2010},
	pages = {270 - 275},
	abstract = {This paper proposes the method to extract the friendship relations of children by using motion sensors. Children learn to fit into society through living in a group, and it{\textquoteright}s greatly influenced by their friend relations. Although preschool teachers need to observe them to assist in the growth of children{\textquoteright}s social progress and support the development each child{\textquoteright}s personality, only experienced teachers can watch over children while providing high-quality guidance. To resolve the problem, this paper proposes a mathematical and objective method that assists teachers with observation. It uses numerical data of activity level recorded by pedometers, and the authors make tree diagram called dendrogram based on hierarchical clustering with recorded activity level. Also, the authors calculate children{\textquoteright}s "breadth" and "depth" of friend relations by using more than one dendrogram. When the authors record children{\textquoteright}s activity level in a certain kindergarten for two months and evaluated the proposed method, the results usually coincide with remarks of teachers about the children.},
	keywords = {Acceleration sensor, Clustering, Friendship relation, Pedometer},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960659287\&partnerID=40\&md5=0f94aed0a83203638341b37b6cb7cc8b},
	author = {Haga, H. and Kaneda, S. and Kono, A. and Shintani, K.}
}
@article {223,
	title = {Spam blog filtering with bipartite graph clustering and mutual detection between spam blogs and words},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Cited By (since 1996):1Export Date: 10 July 2014},
	month = {2010},
	pages = {108 - 116},
	abstract = {This paper proposes a mutual detection mechanism between spam blogs and words with bipartite graph clustering for filtering spam blogs from updated blog data. Spam blogs are problematic in extracting useful marketing information from the blogosphere; they often appear to be rich sources of information based on individual opinion and social reputation. One characteristic of spam blogs is copied-and-pasted articles based on normal blogs and news articles. Another is multiple postings of the same article to increase the chances of exposure and income from advertising. Because of these characteristics, spam blogs share common words, and such blogs and words can form large spam bi-clusters. This paper explains how to detect spam blogs and spam words with mutual filtering based on such clusters. It reports that the maximum precision, or F-measure, of the filtering is 95\%, based on a preliminary experiment with approximately six months{\textquoteright} updated blog data and a more detailed experiment with one day{\textquoteright}s data. An advantage of this method for spam blog filtering, as compared to a machine learning approach, is also supported by experiments with SVM.},
	keywords = {Bipartite graph, Clustering, Filtering, Mutual detection, Spam blog, Spam word},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960276152\&partnerID=40\&md5=575c9621399a49b24a9d7c264810d304},
	author = {Ishida, K.}
}
@article {158,
	title = {Automating the management of scientific conferences using information mining techniques},
	journal = {Journal of Digital Information Management},
	volume = {6},
	year = {2008},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2008},
	pages = {3 - 11},
	abstract = {Conference management systems, which help organizers in carrying out tasks in the workflow, of scientific events are widely used in the academic world. In this paper we focus on tasks where methods from the domain of information retrieval, information management and information organization can assist the organizer, the program committee members and the participants. We present a method for the creation of an improved review process by better matching the reviewers expertise with the paper topics, which can to increase the quality of the conference. Furthermore the conference participants benefit from better access to the wealth of information accumulated throughout a conference series. The Conference organizers profit from the reduced workload because of the partial automating of tedious tasks, such as the review assignment, the compilation of the conference program and the creation of poster setup plans. We report on case studies from a small-sized (around 200 participants), a medium-sized (around 400 participants) as well as a large (more than 700 participants) conference in the computer science as well as the medical domains.},
	keywords = {Assignment problem, Automation, Clustering, Information visualization, Self-organizing maps},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-56849090253\&partnerID=40\&md5=bcbf62b3dabc739191df5c6d0fcdad50},
	author = {Pesenhofer, A. and Mayer, R. and Rauber, A.}
}
@article {67,
	title = {Sensitivity analysis of server placement on enterprise network topology through soft computing},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Export Date: 10 July 2014},
	month = {2007},
	pages = {1 - 7},
	abstract = {We analyze the consequences when both the server placement problem and network topology problem are solved concurrently through soft computing. Both problems are formulated as a combined optimization problem, subject to a set of design and performance constraints while minimizing the enterprise network cost. We have coded the combined optimization problem within a soft computing methodology, which is based on a probabilistic genetic program for automatically searching the design space for good network topologies. The experimental results for synthesizing and optimizing 3-level enterprise network (65 user nodes) for an edge-server placement has demonstrated the effectiveness of our methodology in finding good solutions with a static workload in less than five minutes.},
	keywords = {Analysis, Clustering, Edge-server placement, Network topology, Optimization, Soft computing, Web structure analysis},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33947320448\&partnerID=40\&md5=62ced3281abb66c04e4ac8f00c808c7b},
	author = {Safar, M. and Habib, S.}
}
