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Enhance the Efficiency of Clustering by Minimizing the Processing Time Using Hadoop MapReduce
Data is increasing day-by-day with the development of information technology. Extracting the required information from huge amount of data is a complex and time consuming process. Clustering can be considered the most important unsupervised learning in data mining. K-means clustering is a traditional and popular cluster analysis method in data...
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Parallelized Contextual Valance Shifter Algorithm for Sentiment Analysis on Big Data
Popular and boundless use of World Wide Web (WWW) leads to generate a huge amount of unstructured data called, 'Big data'. Such data are rich in knowledge that plays a vital role in the process of decision-making. Knowledge can also be extracted in the form of sentiment polarity by the...
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Big Data Analytics with Map Reduce and Hadoop
Recently, big data has attracted a lot of attention from academia, industry as well as government. It is a very challenging research area. Big data is term defining collection of large and complex data sets that are difficult to process using conventional data processing tools. Every day, the people create...
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Apache Hadoop 2 (Hadoop 2.0) is the second iteration of the Hadoop framework for distributed data processing Hadoop 2 adds support for running non-batch applications through the introduction of YARN, a redesigned cluster resource manager that eliminates Hadoop's sole reliance on the Map Reduce programming model. Short for yet another...
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A Survey on Data Stream and Its Various Techniques
Data stream mining is become new emerging topic for research in knowledge discovery. In this continuous changing nature of data creates problem in mining the knowledge from it and its difficult to store. There are some techniques and algorithms which are using for mining in the data stream like classification,...
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Now-a-days, Collaborative Filtering (CF) is the most accepted recommendation technique, however many CF systems suffer from issues like data rating availableness and space dimensionality for neighborhood choice. Therefore, using clustering techniques is a way to reduce time needed for processing these correlations. In this paper, a hybrid Agglomerative Hierarchical Cluster...
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A Survey and Cost Classification of Big Data Analytics and Decision Tools
Big data is changing the way business decisions are made and it's still early in the game. However, big data demands new problem-solving approaches because it exceeds the capacity and capabilities of conventional storage, reporting and analytics systems. Hence, prompting the need for sophisticated data analytics tools for proper processing...
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Algometric Solution for Creation of Data Warehouse
As on date when data warehouse is necessitated as an information repository, design of data warehouse is not standardized and development of warehouse is primarily treated as Extract, Transform & Load (ETL) project, just like a typical software development project. Even after years of successful development in data warehousing across...
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Data Flow of Motivated Data Using Heterogeneous Method for Complexity Reduction
Big data is huge data sets with sizes above the ability of commonly used software tools to manage and process the data within a tolerable minimum time. The era of big data has arrived astonishingly in the past few years. Numerous data are produced in the form of documents, chatting...
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Design and Development of Novel Sentence Clustering Technique for Text Mining
Clustering is the process of grouping or aggregating of data items. The sentence clustering is used in variety of applications i.e. classify and categorization of documents, automatic summary generation, etc. In text mining, the sentence clustering plays a vital role this is used in text activities. Size of clusters can...
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