Book Image

Clojure for Data Science

By : Garner
Book Image

Clojure for Data Science

By: Garner

Overview of this book

The term “data science” has been widely used to define this new profession that is expected to interpret vast datasets and translate them to improved decision-making and performance. Clojure is a powerful language that combines the interactivity of a scripting language with the speed of a compiled language. Together with its rich ecosystem of native libraries and an extremely simple and consistent functional approach to data manipulation, which maps closely to mathematical formula, it is an ideal, practical, and flexible language to meet a data scientist’s diverse needs. Taking you on a journey from simple summary statistics to sophisticated machine learning algorithms, this book shows how the Clojure programming language can be used to derive insights from data. Data scientists often forge a novel path, and you’ll see how to make use of Clojure’s Java interoperability capabilities to access libraries such as Mahout and Mllib for which Clojure wrappers don’t yet exist. Even seasoned Clojure developers will develop a deeper appreciation for their language’s flexibility! You’ll learn how to apply statistical thinking to your own data and use Clojure to explore, analyze, and visualize it in a technically and statistically robust way. You can also use Incanter for local data processing and ClojureScript to present interactive visualisations and understand how distributed platforms such as Hadoop sand Spark’s MapReduce and GraphX’s BSP solve the challenges of data analysis at scale, and how to explain algorithms using those programming models. Above all, by following the explanations in this book, you’ll learn not just how to be effective using the current state-of-the-art methods in data science, but why such methods work so that you can continue to be productive as the field evolves into the future.
Table of Contents (12 chapters)
11
Index

The drawbacks of k-means

k-means is one of the most popular clustering algorithms due to its relative ease of implementation and the fact that it can be made to scale well to very large datasets. In spite of its popularity, there are several drawbacks.

k-means is stochastic, and does not guarantee to find the global optimum solution for clustering. In fact, the algorithm can be very sensitive to outliers and noisy data: the quality of the final clustering can be highly dependent on the position of the initial cluster centroids. In other words, k-means will regularly discover a local rather than global minimum.

The drawbacks of k-means

The preceding diagram illustrates how k-means may converge to a local minimum based on poor initial cluster centroids. Non-optimal clustering may even occur if the initial cluster centroids are well-placed, since k-means prefers clusters with similar sizes and densities. Where clusters are not approximately equal in size and density, k-means may fail to converge to the most natural...