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Data Mining

Personal Data – Tracked and mined by mobile apps

While malware still remains to be a major concern for mobile devices a new study suggests that there are more severe threats lurking within your mobile apps. Data mining via intrusive mobile apps has become a topic of debate. There are a number of mobile apps that exhibit risky behavior when it comes to sharing personal information – that includes accessing user’s contacts, calendars, locations and more. The unwarranted data mining from apps is more of a threat to users than any malware. So, why should we care if any of the (seemingly innocent) app can access your contact list or look at your purchase history? You should care because, this unprotected flow of user data over unprotected networks could...


Data Science Techniques for Efficient Data Mining

As more and more organizations rely on analytics to help gain insights into problems, they require efficient means to gain insights into the diverse types of data available. Finding and extracting intelligence from the available data has thus become essential. Data science techniques can help structure this data from different sources. Techniques for handling data from text, sequences, time series, space and graphs etc. Help increase the power and accuracy of analytic solutions. Mining different kinds of knowledge in a database Data mining should cover a large spectrum of data analysis and knowledge discovery tasks. These include: data characterization, separation, association and correlation analysis, classification, prediction, clustering, outlier analysis, and evolution analysis (including also trend and similarity analysis). All these...