From Triconcepts to Triclusters
A vast amount of documents in the Web have duplicates, which is a challenge for developing efficient methods that would compute clusters of similar documents. In this paper we use an approach based on computing (closed) sets of attributes having large support (large extent) as clusters of similar documents. The method is tested in a series of computer experiments on large public collections of web documents and compared to other established methods and software, such as biclustering, on same datasets. Practical efficiency of different algorithms for computing frequent closed sets of attributes is compared.
Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classication, introduced and detailed in the book of Bernhard Ganter and Rudolf Wille, \Formal Concept Analysis", Springer 1999. The area came into being in the early 1980s and has since then spawned over 10000 scientic publications and a variety of practically deployed tools. FCA allows one to build from a data table with objects in rows and attributes in columns a taxonomic data structure called concept lattice, which can be used for many purposes, especially for Knowledge Discovery and Information Retrieval. The \Formal Concept Analysis Meets Information Retrieval" (FCAIR) workshop collocated with the 35th European Conference on Information Retrieval (ECIR 2013) was intended, on the one hand, to attract researchers from FCA community to a broad discussion of FCA-based research on information retrieval, and, on the other hand, to promote ideas, models, and methods of FCA in the community of Information Retrieval. This volume contains 11 contributions to FCAIR workshop (including 3 abstracts for invited talks and tutorial) held in Moscow, on March 24, 2013. All submissions were assessed by at least two reviewers from the program committee of the workshop to which we express our gratitude. We would also like to thank the co-organizers and sponsors of the FCAIR workshop: Russian Foundation for Basic Research, National Research University Higher School of Economics, and Yandex.
Formal Concept Analysis (FCA) is a mathematical technique that has been extensively applied to Boolean data in knowledge discovery, information retrieval, web mining, etc. applications. During the past years, the research on extending FCA theory to cope with imprecise and incomplete information made significant progress. In this paper, we give a systematic overview of the more than 120 papers published between 2003 and 2011 on FCA with fuzzy attributes and rough FCA. We applied traditional FCA as a text-mining instrument to 1072 papers mentioning FCA in the abstract. These papers were formatted in pdf files and using a thesaurus with terms referring to research topics, we transformed them into concept lattices. These lattices were used to analyze and explore the most prominent research topics within the FCA with fuzzy attributes and rough FCA research communities. FCA turned out to be an ideal metatechnique for representing large volumes of unstructured texts.
This book constitutes the refereed proceedings of the 20th International Symposium on String Processing and Information Retrieval, SPIRE 2013, held in Jerusalem, Israel, in October 2013. The 18 full papers, 10 short papers were carefully reviewed and selected from 60 submissions. The program also featured 4 keynote speeches. The following topics are covered: fundamentals algorithms in string processing and information retrieval; SP and IR techniques as applied to areas such as computational biology, DNA sequencing, and Web mining.
The way of the automated knowledge control system realization is offered on the basis of such intellectual means as the ontologic approach, fuzzy logic and data mining.
Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.
In this paper some of the task assignment methods and approaches are examined. The analysis of the algorithms considered is showing their strengths and weaknesses. Also, the ways of further research are presented, which aims to develop a methodology for task assignment in project management area.
Doctoral students were invited to the Doctoral Consortium held in conjunction with the main conference of ECIR 2013. The Doctoral Consortium aimed to provide a constructive setting for presentations and discussions of doctoral students’ research projects with senior researchers and other participating students. The two main goals of the Doctoral Consortium were: 1) to advise students regarding current critical issues in their research; and 2) to make students aware of the strengths and weakness of their research as viewed from different perspectives. The Doctoral Consortium was aimed for students in the middle of their thesis projects; at minimum, students ought to have formulated their research problem, theoretical framework and suggested methods, and at maximum, students ought to have just initiated data analysis. The Doctoral Consortium took place on Sunday, March 24, 2013, at the ECIR 2013 venue, and participation is by invitation only. The format was designed as follows: The doctoral students presents summaries of their work to other participating doctoral students and the senior researchers. Each presentation was followed by a plenary discussion, and individual discussion with one senior advising researcher. The discussions in the group and with the advisors were intended to help the doctoral student to reflect on and carry on with their thesis work.