In data mining two important tasks involved are classification and clustering. In general, in classification the classifier assigns a class label from a set of predefined classes to a new input object. Whereas, given a set of objects, clustering creates different groups of these objects using some similarity measure. In the context of machine learning, classification is supervised learning and ...

In data mining two important tasks involved are classification and clustering. In general, in classification the classifier assigns a class label from a set of predefined classes to a new input object. Whereas, given a set of objects, clustering creates different groups of these objects using some similarity measure. In the context of machine learning, classification is supervised learning and ...

In data mining two important tasks involved are classification and clustering. In general, in classification the classifier assigns a class label from a set of predefined classes to a new input object. Whereas, given a set of objects, clustering creates different groups of these objects using some similarity measure. In the context of machine learning, classification is supervised learning and ...

We introduce a new notion of fuzzy r -interior which is an extension of Chang’s fuzzy interior. Using fuzzy r -interior, we define fuzzy r -semiopen sets and fuzzy r semicontinuous maps which are generalizations of fuzzy semiopen sets and fuzzy semicontinuous maps in Chang’s fuzzy topology, respectively. Some basic properties of fuzzy r -semiopen sets and fuzzy r -semicontinuous maps are invest...

A systematic approach to the inventory control and classification may have a significant influence on company competitiveness. In practice, all inventories cannot be controlled with equal attention. In order to efficiently control the inventory items and to determine the suitable ordering policies for them, multiple criteria inventory classification is used. In this paper, a systematic and logi...

Artificial intelligent techniques have been widely used in high voltage insulation technology application. In this paper, the effectiveness of artificial intelligent technique to apply for pattern recognition and classification of Partial Discharge (PD) is presented. Partial discharge signal was generated and measured by using an artificial partial discharge source. Characteristics of PD signal...

In the present contribution we propose two recently developed classification algorithms for the analysis of mass-spectrometric data-the supervised neural gas and the fuzzy-labeled self-organizing map. The algorithms are inherently regularizing, which is recommended, for these spectral data because of its high dimensionality and the sparseness for specific problems. The algorithms are both proto...

Pattern classification is one of the most important topics for machine learning research fields. However incomplete data appear frequently in real world problems and also show low learning rate in classification models. There have been many researches for handling such incomplete data, but most of the researches are focusing on training stages. In this paper, we proposed two classification meth...

We define interval valued (∈,∈∨q)-fuzzy k-ideals, interval valued (∈,∈∨q)-fuzzy k-quasi-ideals, interval valued (∈,∈∨q)-fuzzy k-bi-ideals and characterize k-regular and k-intra regular hemirings by the properties of interval valued (∈,∈∨q)-fuzzy k-ideals, interval valued (∈,∈∨q)-fuzzy k-quasi-ideals and interval valued (∈,∈∨q)-fuzzy k-bi-ideals. 2010 mathematics subject classification: 16Y60 • ...

Fuzzy neural networks (FNNs) provide a new approach for classification of multispectral data and to extract and optimize classification rules. Neural networks deal with issues on a numeric level, whereas fuzzy logic deals with them on a semantic or linguistic level. FNNs synthesize fuzzy logic and neural networks. Recently, there has been growing interest in the research community not only to u...