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The previous studies focus on how to find frequent patterns from large traffic and sensor data based applications. Big Data mining is the ability to extract valuable information either from these huge datasets or else streams of data, due to its three V's (Volume, Velocity, and Variety).
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Developers cannot supervise them through conventional algorithms or Knowledge discovery software tools. Big Data is a latest tendency used to examine the datasets from large, complex databases. Mining frequent and maximal Periodic Patterns is the challenging task for the data scientists due to unstructured, dynamic and huge raw data generated from web. The paper explains these approaches by identifying various dimensionality reduction techniques to improve the performance. It is understood that there are several techniques such as Sequential Forward Selection(SFS), Sequential Floating Forward Selection(SFFS) and Random Subset Feature Selection (RSFS) etc., which are used to minimize the storage space of the scientific data set in combination of Nearest Neighbor classifier. The performances of the techniques are compared and a meaningful direction has been arrived.
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This paper presents a review and systematic comparative study of methods and techniques used in scientific data mining. Dimensionality reduction technique on scientific data is a popular area to understand the underlying scientific knowledge in a data set, resulted from scientific experiments. Using the lazy learning classification algorithm, the feature selection methods calculate relevancy to reduce the storage. This paper also describes the extraction of dynamic patterns, future structures which aids in retention of customer and managing the market requirement more efficiently.įeature Selection(FS) is an important step to enhance the classification accuracy. The analysis on the centrality measure are made to identify the central and most influential customers in the network, to provide customized services for retention of that customer. This approach deals with finding the social position of the customer by different measures like centrality, betweenness, density and closeness. The telecom network is constructed by using adjacency matrix for all the customers. The telecom social network is constructed by considering multiple attributes and different services provided by the telecom industry. This paper addresses an efficient method of building multi-relational and heterogeneous social network for telecom customers and identifying social structures present in the telecommunication network. It is proved to be one of the most effective ways to analyze and extract the overall users' view about the particular feature and whole product as well.Įxtracting the customers who share similar interests that are connected via set of relationships in Telecom social network is a challenging scenario. In our work we have developed an overall process of 'Aspect or Feature based Sentiment Analysis' by using a classifier called Support Vector Machine (SVM) in a novel approach. This problem can be addressed by an automated system called 'Sentiment Analysis and Opinion Mining' that can analyze and extract the users' perception in the whole reviews. However, it would be a tedious task to manually extract overall opinion out of enormous unstructured data. So that the manufacturer can modify the features of the product as required and on the other hand these will also help the new consumers to decide on whether to buy the product or not.
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These reviews play a vital role in determining how far a product has been placed in consumers' psyche.
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Every day people buy many products through online and post their reviews about the product which they have used. In this modern era of globalization, e-commerce has become one of the most convenient ways to shop.