K-means is a centroid based algorithm that means points are grouped in a cluster according to the distance(mostly Euclidean) from centroid.
K-means is a centroid based algorithm that means points are grouped in a cluster according to the distance(mostly Euclidean) from centroid.
Added by satyajit maitra on July 1, 2019 at 6:30am — No Comments
1. Introduction
Data science is changing the rules of the game for decision making. Artificial intelligence is living its golden years where abundance of data, cheap computing capacity, and devoted talent depicts an unstoppable intelligence assisted life for humans. While it is common to hear about AI advice on health or financial investments, the same in business strategy is not so common. Maybe it is just a matter of time that AI learns how to handle data to support…
ContinueAdded by Ramon Serrallonga on January 9, 2019 at 9:00am — 3 Comments
Social media provide a low-cost alternative source for public health surveillance and health-related classification plays an important role to identify useful information. We summarized the recent classification methods using social media in public health. These methods rely on bag-of-words (BOW) model and have difficulty grasping the semantic meaning of texts. Unlike these methods, we present a word embedding based clustering method. Word embedding is one of the strongest trends in Natural…
ContinueSummary: Unless you’re involved in anomaly detection you may never have heard of Unsupervised Decision Trees. It’s a very interesting approach to decision trees that on the surface doesn’t sound possible but in practice is the backbone of modern intrusion detection.
I was at a presentation recently that focused on stream processing but the use case presented was about anomaly detection. When they started talking about unsupervised decision trees my…
ContinueAdded by William Vorhies on October 17, 2017 at 8:30am — 6 Comments
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