Assumptions Can Ruin Your K-Means Clusters

Clustering is one of the most powerful and widely used of the machine learning techniques. It’s very seductive. Throw some data into the algorithm and let it discover hitherto unknown relationships and patterns. K-means is the most popular of all the cluster algorithms. It’s easy to understand—and therefore implement—so it’s available in almost all analysis […]
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5 Machine Learning Books Worth Reading

Machine learning remains a hot topic as organizations attempt to squeeze insights and competitive advantage from their data sets. I’ve selected five books that may be of interest if you are embarking on a machine learning initiative or career. Obviously, there are much more great books out there and I’d like to hear your recommendations—recommendation […]
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5 Big Data Security Challenges

As interest in big data has exploded, organizations have rushed to grab competitive advantage by deploying analytics pipelines that exploit this newly available resource. Many projects have been set up in a “skunkworks” environment, often by data science teams. While this has accelerated the time to market for new features, it has created a potential […]
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Does the object-oriented paradigm deliver on its promises?

I’ve been programming using the object-oriented (OO) paradigm for most of my career. It’s the dominant paradigm in enterprise software development and has been for some time. I’ve used, and use, other paradigms, such as functional programming, but, like most software developers, the bulk of my time as been spent using OO languages, such as […]
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Tackling a new dataset

With all the excitement (hype?) surrounding machine learning, there’s a tendency for decision makers (and many analysts) to believe that insights are just waiting to spring forth from the data when they are finally released by these magical algorithms. When it’s time to sit down and actually work with a real dataset, disillusionment quickly sets […]
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