49,367 research outputs found
Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images
We propose a novel scheme for designing fuzzy rule based classifier. An SOFM
based method is used for generating a set of prototypes which is used to
generate a set of fuzzy rules. Each rule represents a region in the feature
space that we call the context of the rule. The rules are tuned with respect to
their context. We justified that the reasoning scheme may be different in
different context leading to context sensitive inferencing. To realize context
sensitive inferencing we used a softmin operator with a tunable parameter. The
proposed scheme is tested on several multispectral satellite image data sets
and the performance is found to be much better than the results reported in the
literature.Comment: 23 pages, 7 figure
Combining quantifications for flexible query result ranking
Databases contain data and database systems governing such databases are often intended to allow a user to query these data. On one hand, these data may be subject to imperfections, on the other hand, users may employ imperfect query preference specifications to query such databases. All of these imperfections lead to each query answer being accompanied by a collection of quantifications indicating how well (part of) a group of data complies with (part of) the user's query. A fundamental question is how to present the user with the query answers complying best to his or her query preferences. The work presented in this paper first determines the difficulties to overcome in reaching such presentation. Mainly, a useful presentation needs the ranking of the query answers based on the aforementioned quantifications, but it seems advisable to not combine quantifications with different interpretations. Thus, the work presented in this paper continues to introduce and examine a novel technique to determine a query answer ranking. Finally, a few aspects of this technique, among which its computational efficiency, are discussed
Spatial database implementation of fuzzy region connection calculus for analysing the relationship of diseases
Analyzing huge amounts of spatial data plays an important role in many
emerging analysis and decision-making domains such as healthcare, urban
planning, agriculture and so on. For extracting meaningful knowledge from
geographical data, the relationships between spatial data objects need to be
analyzed. An important class of such relationships are topological relations
like the connectedness or overlap between regions. While real-world
geographical regions such as lakes or forests do not have exact boundaries and
are fuzzy, most of the existing analysis methods neglect this inherent feature
of topological relations. In this paper, we propose a method for handling the
topological relations in spatial databases based on fuzzy region connection
calculus (RCC). The proposed method is implemented in PostGIS spatial database
and evaluated in analyzing the relationship of diseases as an important
application domain. We also used our fuzzy RCC implementation for fuzzification
of the skyline operator in spatial databases. The results of the evaluation
show that our method provides a more realistic view of spatial relationships
and gives more flexibility to the data analyst to extract meaningful and
accurate results in comparison with the existing methods.Comment: ICEE201
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