Fuzzy clustering-based discretization for gene expression classification

Kianmehr, Keivan; Alshalalfa, Mohammed; Alhajj, Reda

doi:10.1007/s10115-009-0214-2

Cited by 28 publications

(12 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fuzzification method reported by Kianmehr et al [48] was used to fuzzy the intervals. Let M i " tm i1 , m i2 , ..., m in u be the set of centroids of the intervals for a condition attribute A i .…”

Section: Attribute Discretization and Fuzzificationmentioning

confidence: 99%

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Yang

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

Urban fringe is the transition zone fine grained with urban and non-urban land cover types. The complex landscape mosaic in this area challenges the land cover classification based on the remote-sensing data. Spectral signatures are not efficient to discriminate all pixels into classes. To improve the recognition and handle the uncertainty, this paper provides a novel integrated approach, based on a fuzzy rough set and evidential reasoning (FRSER), for land cover classification in an urban fringe area. The approach is implemented on Landsat Operation Land Imager data covering the urban fringe area of Wuhan city, China. A fuzzy rough set is first used to define a decision table from multispectral imagery and ground reference data. Then the fuzzy rough information system is interpreted using the Dempster-Shafer theory, based on an evidential reasoning system. A final land cover classification with uncertainty is achieved by evidential reasoning. The results are compared with the traditional maximum likelihood classifier (MLC) and some rough set-based classifiers including classical rough set classifier (RS), fuzzy rough set classifier (FRS), and variable precision fuzzy rough set classifier (VPFRS). The better overall accuracy, user's and producer's accuracies, and the kappa coefficient, in comparison with the other classifiers, suggest that the proposed approach can effectively discriminate land cover types in urban fringe areas with high inter-class similarities and intra-class heterogeneity. It is also capable of handling the uncertainty in data processing, and the final land cover map comes with a degree of uncertainty. The proposed approach that can efficiently integrate the merits of both the fuzzy rough set and DS theory provides an efficient method for urban fringe land cover classification.

show abstract

Section: Attribute Discretization and Fuzzificationmentioning

confidence: 99%

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Yang

et al. 2016

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Cluster analysis methods have been applied in different fields such as engineering, social science, medical sciences, economics, etc. For more detailed description of clustering techniques the interested reader can refer to [102] and to [95]. In the previous subsection we presented the distance ratio algorithm to find the distance between two FCMs.…”

Section: Hierarchical Clustering Approachmentioning

confidence: 99%

“…The most widely used linkage methods are: single, complete and ward linkage methods [102]. The ward method is not efficient for our study as we do not use the Euclidian distance for similarity measurement.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

“…Single-linkage suffers from a chaining effect producing elongated clusters. The study in [102] shows that the complete-linkage produces more compact and more useful hierarchies in many applications than the single-linkage algorithm. Besides to decide which algorithm suits the data of the study best, we calculated the Cophenetic Coefficient (CC) for single, complete, weighted, average, ward and centroid linkage methods.…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

See 1 more Smart Citation

Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps

León¹,

Nápoles²,

Bello³

et al. 2013

IJCIS

View full text Add to dashboard Cite

Although the individuals" transport behavioural modelling is a complex task, it can produce a notable social and economic impact. In this paper, Fuzzy Cognitive Maps are explored to represent the behaviour and operation of such complex systems. An automatic approach to extract mental representations from individuals and convert them into computational structures is defined. For the creation of knowledge bases the use of Knowledge Engineering is accounted and later on the data is transferred into structures based on Fuzzy Cognitive Maps. Once the maps are created, their performances get improved through the use of a Particle Swarm Optimisation algorithm as a learning method, readjusting its predicting capacity from stored scenarios, where individuals left their preferences in front of random situations. Another important result is clustering the maps for knowledge discovery. This permits to find useful groups of individuals that policymakers can use for simulating new rules and policies. After related maps are identified, to merge them as a unique structure could benefit for different usages. Therefore an aggregating procedure is elaborated for this task, constituting an alternative approach for selecting a centroid of a specific estimated group, and therefore having, in only one structure, the knowledge and behavioural acting from a collection of individuals. Learning, clustering and aggregation of Fuzzy Cognitive Maps are combined in a cascade experiment, with the intention of describing travellers" behaviour and change trends in different abstraction levels. The results of this approach will help transportation policy decision makers to understand the people"s needs in a better way, consequently will help them actualising different policy formulations and implementations.

show abstract

“…The objective of data mining is to extract comprehensible, useful, and non-trivial knowledge from large datasets. Several data mining techniques [5,17,40] are used to extract such knowledge of interest as clustering [1,10,23], classification [24], frequent-pattern mining [36], association rules mining (ARM) [2,33,38], etc.…”

Section: Introductionmentioning

confidence: 99%

Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules

2011

View full text Add to dashboard Cite

This paper presents a proposal for the extraction of association rules called G3PARM (Grammar-Guided Genetic Programming for Association Rule Mining) that makes the knowledge extracted more expressive and flexible. This algorithm allows a context-free grammar to be adapted and applied to each specific problem or domain and eliminates the problems raised by discretization. This proposal keeps the best individuals (those that exceed a certain threshold of support and confidence) obtained with the passing of generations in an auxiliary population of fixed size n. G3PARM obtains solutions within specified time limits and does not require the large amounts of memory that the exhaustive search algorithms in the field of association rules do. Our approach is compared to exhaustive search (Apriori and FP-Growth) and genetic (QuantMiner and ARMGA) algorithms for mining association rules and performs an analysis of the mined rules. Finally, a series of experiments serve to contrast the scalability of our algorithm. The proposal obtains a small set of rules with high support and confidence, over 90 and 99% respectively. Moreover, the resulting set of rules closely satisfies all the dataset instances. These results illustrate that our proposal is highly promising for the discovery of association rules in different types of datasets.

show abstract

Fuzzy clustering-based discretization for gene expression classification

Cited by 28 publications

References 21 publications

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Tackling Travel Behaviour: An approach based on Fuzzy Cognitive Maps

Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules

Contact Info

Product

Resources

About