In this paper a review of type-2 fuzzy logic applications in pattern recognition, classification and clustering problems is presented. Recently, type-2 fuzzy logic has gained popularity in a wide range of applications due to its ability to handle higher degrees of uncertainty. In particular, there have been recent applications of type-2 fuzzy logic in the fields of pattern recognition, classifi...

In this contribution, a nearly comprehensive survey among creators and performers in media, arts and entertainment in the Netherlands is presented. It concerns the implications of digital reproduction and distribution for the creative professions as perceived by those working in it. Based on regressions and cluster analysis of the survey data, an analysis is provided of income developments and ...

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. Diffusion Maps (DM) were selected as the method of choice for performing dimensionality reduction on text records for this study. Diffusion Maps have seen successful use in other domains such as image classification and pattern recognition. High...

A new densityand grid-based type clustering algorithm using the concept of shifting grid is proposed. The proposed algorithm is a non-parametric type, which does not require users inputting parameters. It divides each dimension of the data space into certain intervals to form a grid structure in the data space. Based on the concept of sliding window, shifting of the whole grid structure is intr...

Many crimes can happen every day in a major city, and figuring out which ones are committed by the same individual or group is an important and difficult data mining challenge. To do this, we propose a pattern detection algorithm called Series Finder, that grows a pattern of discovered crimes from within a database, starting from a “seed” of a few crimes. Series Finder incorporates both the com...

This paper presents an algorithm which learns a distance metric from a data set by knowledge embedding and uses the new distance metric to solve nonlinear pattern recognition problems such a clustering. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Computers are still much less useful than the ability of the human eye for pattern matching. This ability can be used quite straightforwardly to identify structure in a data set when it is two or three dimensional. With data sets with more than 3 dimensions some kind of transformation is always necessary. In this paper we review in depth and present and extension of one of these mechanisms: And...

A new approach to the recognition problem is considered, which does not require clustering of the sampling space into the appropriate number of regions. Each sample mapped in multi-dimensional space is represented by a family of points (vectors), forming a certain configuration. Less compact location of the points leads to more simple and reliable pattern recognition. The input description is r...

Making use of unlabeled data samples in a classification or training of a classifier is a desirable aim for many real world applications in pattern recognition. In this study, a multiple classifier system is utilized to investigate this matter. Further, cluster analysis is used in order to group the available data while neglecting the actual labels. Then, by implementing an information fusion a...

Aspergillus Xavus populations are genetically diverse. Isolates that produce either, neither, or both aXatoxins and cyclopiazonic acid (CPA) are present in the Weld. We investigated defects in the aXatoxin gene cluster in 38 nonaXatoxigenic A. Xavus isolates collected from southern United States. PCR assays using aXatoxin-gene-speciWc primers grouped these isolates into eight (A–H) deletion pat...