ABSTRACT

The task of outlier detection is to find the small groups of data objects that are exceptional to the inherent behavior of the rest of the data. Detection of such outliers is fundamental to a variety of database and analytic tasks such as fraud detection and customer migration. There are several approaches [10] of outlier detection employed in many study areas amongst which distance based and density based outlier detection techniques have gathered most attention of researchers. In informat ion theory, entropy is a core concept that measures uncertainty about a stochastic event, and it means that entropy describes the distribution of an event. Because of its ability to describe the distribution of data, entropy has been applied in clustering applications in data mining. In this paper, we have developed a robust supervised outlier detection algorith m using hybrid approach (RODHA) which incorporates both the concept of distance and density along with entropy measure while determining an outlier. We have provided an empirical study of different existing outlier detection algorithms and established the effectiveness of the proposed RODHA in co mparison to other outlier detection algorith ms.

Human gait reveals feelings, intentions and identity which is perceived by most human beings.To understand this perceptual ability, Swedish psychologist Gunnar Johansson (1973)

Gesture recognition means the identification of different expressions of human body parts to express the idea, thoughts and emotion. It is a multi-disciplinary research area. The application areas of gesture recognition have been spreading very rapidly in our real-life activities including dance gesture recognition. Dance gesture recognition means the recognition of meaningful expression from the different dance poses. Today, research on dance gesture recognition receives more and more attention throughout the world. The automated recognition of dance gestures has many applications. The motive behind this survey is to present a comprehensive survey on automated dance gesture recognition with emphasis on static hand gesture recognition. Instead of whole body movement, we consider human hands because human hands are the most flexible part of the body and can transfer the most meaning. A list of research issues and open challenges is also highlighted.

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