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With advances in technology, high volumes of a wide variety of valuable data of different veracity can be easily collected or generated at a high velocity in the current era of big data. Embedded in these big data are implicit, previously unknown and potentially useful information. Hence, fast and scalable big data science and engineering solutions that mine and discover knowledge from these big data...
Distributed Applications from different domains like Health care, E-Commerce, science, social networks etc., tend to generate large volumes of heterogeneous data that grow exponentially over a period of time leading to big data sets. Descriptive Analytics, on big data sets, pose a great challenge for traditional data analytical tools, since it is to be performed on the full data set, unlike predictive...
In recent years the amount of data stored in educational database is growing rapidly. The stored database contains hidden information which if used aids improvement of student's performance and behaviour. In this paper predictive modelling approach is used for extracting this hidden information. Data is collected, a predictive model is formulated, predictions are made, and the model is validated as...
Big data is a broad data set that has been used in many fields. To process huge data set is a time consuming work, not only due to its big volume of data size, but also because data type and structure can be different and complex. Currently, many data mining and machine learning technique are being applied to deal with big data problem; some of them can construct a good learning algorithm in terms...
K-means is the most widely used clustering algorithm due to its fairly straightforward implementations in various problems. Meanwhile, when the number of clusters increase, the number of iterations also tend to slightly increase. However there are still opportunities for improvement as some studies in the literature indicate. In this study, improved implementations of k-means algorithm with a centroid...
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