Concepts of graph theory have applications in many areas of computer science including data mining, image segmentation, clustering, image capturing, networks, etc . An interval-valued fuzzy set is a generalization of the notion of a fuzzy set. Interval-valued fuzzy models give more precision, flexibility and compatibility to the system as compared to the fuzzy models. In this paper, we introduc...

Data mining is a relatively new data analysis technique that has the ability to discover patterns stored within historical data and is now considered a catalyst for enhancing business processes by avoiding failure patterns and exploiting success patterns. This technique is widely used in business applications including market segmentation, fraud detection, and credit risk analysis as well as ma...

Clustering is a useful approach in image segmentation, data mining and other pattern recognition problems for which unlabeled data exist. Fuzzy clustering using fuzzy c-means or variants of it can provide a data partition that is both better and more meaningful than hard clustering approaches. The clustering process can be quite slow when there are many objects or patterns to be clustered. This...

Abstract. Theoretical concepts of graphs are highly utilized by computer scientists. Especially in research areas of computer science such as data mining, image segmentation, clustering image capturing and networking. The interval-valued fuzzy graphs are more flexible and compatible than fuzzy graphs due to the fact that they allowed the degree of membership of a vertex to an edge to be represe...

business related decisions become more perplexing as societies advance. environmental ambiguities which stem from new business models intensify the above mentioned complexity so far as many of the affecting variables and their interrelationships are non linear and complicated. in this circumstances data analysis and knowledge extraction is not practical via traditional methods. this research co...

Time series clustering is an important data mining topic and a challenging task due to the sequences’ potentially very complex structures. In the present study we experimentally investigate the combination of support vector clustering with a triangular alignment kernel by evaluating it on an artificial time series benchmark dataset. The experiments lead to meaningful segmentations of the data, ...

The problem this paper focuses on is the classical problem of unsupervised clustering of a data-set. In particular, the bisecting divisive clustering approach is here considered. This approach consists in recursively splitting a cluster into two sub-clusters, starting from the main data-set. This is one of the more basic and common problems in fields like pattern analysis, data mining, document...

Time series is an important class of temporal data objects and it can be easily obtained from scientific and financial applications. A time series is a collection of observations made chronologically. The nature of time series data includes: large in data size, high dimensionality and necessary to update continuously. Moreover time series data, which is characterized by its numerical and contin...

Data mining is a well-known technique, which can be used to extract hidden information about customers’ behaviors. It is used to improve the customer relationship management processes by various Organizations. Previous researches in constraint based pattern mining emphasis only on the concept of frequency.But the changes in the environment may occur frequently, so the frequently occuring patter...

Text mining is about inferring structure from sequences representing natural language text, and may be defined as the process of analyzing text to extract information that is useful for particular purposes. Although hand-crafted heuristics are a common practical approach for extracting information from text, a general, and generalizable, approach requires adaptive techniques. This paper studies...