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Multimedia data mining is a popular research domain which helps to extract interesting knowledge from multimedia data sets such as audio, video, images, graphics, speech, text and combination

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Since the computerized applications are used all around the world, there occurs the collection of a vast amount of data. The important information hidden in vast data is attracting the researchers of multiple disciplines to make study in developing effective approaches to derive the hidden knowledge within them. Data mining may be considered to be the process of extracting or mining the useful and valuable knowledge from large amounts of data. There are various different domains in data mining such as text mining, image mining, sequential pattern mining, web mining and etc. Among these, sequence mining is one of the most important research area which helps to finding the sequential relationships found in the data. Sequence mining is applied in wide range of application areas such as the analysis of customer purchase patterns, web access patterns, weather observations, protein sequencing, DNA sequencing, etc. In protein and DNA analysis, sequence mining techniques are used for sequence alignment, sequence searching and sequence classification. In the area of protein sequence analysis, the researchers are showing their interest in the field of protein sequence classification. It has the ability to discover the recurring structures that exist in the protein sequences. This paper explains various techniques used by different researchers in classifying the proteins and also provides an overview of different protein sequence classification methods.

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In current years, data streams have been gradually turn into most important research area in the field of computer science. Data streams are defined as fast, limitless, unbounded, river flow, continuous, stop less, massive, tremendous unremitting, immediate, stream flow, arrival of ordered and unordered data. Data is a need for new techniques and algorithms. The main objective of this research work is to perform the clustering process in data streams and detecting the outliers in data streams. New hybrid approach is proposed which combines the hierarchical clustering algorithm and partitioning clustering algorithm. In hierarchical clustering, CURE algorithm is used and enhanced (E-CURE) and in partitioning clustering, CLARANS algorithm is used and enhanced (E-CLARANS). In this research work, the two algorithms E-CURE and E-CLARANS

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