12,051 research outputs found
Comparison of different strategies of utilizing fuzzy clustering in structure identification
Fuzzy systems approximate highly nonlinear systems by means of fuzzy "if-then"
rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification. Basically, there are three different approaches in which one can utilize fuzzy clustering; the �first one is based on input space clustering, the second one considers clustering realized in the output space, while the third one is concerned with clustering realized in the combined input-output space. In this study, we analyze these three approaches. We discuss each of the algorithms in great detail and o¤er a thorough comparative analysis. Finally, we compare the performances of these algorithms in a medical diagnosis classi�cation problem, namely Aachen Aphasia Test. The experiment and the results provide a valuable insight about the merits and the shortcomings of these three clustering approaches
Implementing imperfect information in fuzzy databases
Information in real-world applications is often
vague, imprecise and uncertain. Ignoring the inherent imperfect
nature of real-world will undoubtedly introduce some deformation of human perception of real-world and may eliminate several
substantial information, which may be very useful in several
data-intensive applications. In database context, several fuzzy
database models have been proposed. In these works, fuzziness
is introduced at different levels. Common to all these proposals is
the support of fuzziness at the attribute level. This paper proposes
first a rich set of data types devoted to model the different kinds
of imperfect information. The paper then proposes a formal
approach to implement these data types. The proposed approach
was implemented within a relational object database model but it
is generic enough to be incorporated into other database models.ou
Some notes on an extended query language for FSM
FSM is a database model that has been recently proposed by the authors. FSM uses basic concepts of
classification, generalization, aggregation and association that are commonly used in semantic modelling and
supports the fuzziness of real-world at attribute, entity, class and relations intra and inter-classes levels. Hence, it
provides tools to formalize and conceptualize real-world within a manner adapted to human perception of and
reasoning about this real-word. In this paper we briefly review basic concepts of FSM and provide some notes on an
extended query language adapted to it.ou
Implementation of an efficient Fuzzy Logic based Information Retrieval System
This paper exemplifies the implementation of an efficient Information
Retrieval (IR) System to compute the similarity between a dataset and a query
using Fuzzy Logic. TREC dataset has been used for the same purpose. The dataset
is parsed to generate keywords index which is used for the similarity
comparison with the user query. Each query is assigned a score value based on
its fuzzy similarity with the index keywords. The relevant documents are
retrieved based on the score value. The performance and accuracy of the
proposed fuzzy similarity model is compared with Cosine similarity model using
Precision-Recall curves. The results prove the dominance of Fuzzy Similarity
based IR system.Comment: arXiv admin note: substantial text overlap with
http://ntz-develop.blogspot.in/ ,
http://www.micsymposium.org/mics2012/submissions/mics2012_submission_8.pdf ,
http://www.slideshare.net/JeffreyStricklandPhD/predictive-modeling-and-analytics-selectchapters-41304405
by other author
The commodity chain of the household: from survey design to policy and practice
Data collection and analysis and policy formulation all require a social unit to be defined, generally called the household. Multidisciplinary evidence shows that households as defined by survey practitioners often bear little resemblance to lived socio-economic units. This study examines how a shared language, the 'household', can generate misunderstandings because different groups with distinctive understandings of the term 'household' are often unaware that others may be using ‘household’ differently. Results from 4 interlinked and iterative methods are presented: review of household survey documentation (1950s-present); ethnographic ground-truthing fieldwork; in-depth key informant interviews; and modelling. Results show that whereas data collectors have a clear idea of what a `household` is, data users are often unaware of the nuances of the constraints imposed by data collection. This has implications for policy planning and practice. What interviewees consider when they think of their household can differ systematically from data collectors' definitions
Penghasilan manual rjngkas penggunaan alat Total Station Sokkia Set5f dan Perisian Sdr Mapping & Design untuk automasi ukur topografi
Projek ini dilaksanakan untuk menghasilkan manual ringkas penggunaan alat Total Station Sokkia SET5F dan Perisian SDR Mapping & Design dalam menghasilkan pelan topografi yang lengkap mengikut konsep field to finish. Manual telah dihasilkan dalam dua bentuk iaitu buku dan CD-ROM. Manual ini telah dinilai berdasarkan data yang diperolehi daripada 7 orang responden melalui kaedah Borang Penilaian Manual. Analisis data dilakukan menggunakan perisian SPSS versi 11.0. Hasil analisis skor min menunjukkan kesemua responden bersetuju bahawa manual dalam bentuk buku ini menarik Min ( M ) ^ ^ dan Sisihan Piawai (SD) = .535 tetapi kurang interaktif (M) = 2.29 dan (SD) = 0.488. Berbanding dengan manual dalam format CD-ROM yang mencatat nilai (M) = 3.57 dan (SD) = 0.535 semua responden bersetuju bahawa manual ini mesra pengguna dan lebih interakti
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