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

Probabilistic methods for large sets

Large sets appear in many contexts in data science. We're likely to encounter them while dealing with users' implicit feedback as previously mentioned, but the approaches described next can be applied to any data that can be represented as a set.

Testing set membership with Bloom filters

Bloom filters are data structures that provide a means to compress the size of a set while preserving our ability to tell whether a given item is a member of the set or not. The price of this compression is some uncertainty. A Bloom filter tells us when an item may be in a set, although it will tell us for certain if it isn't. In situations where disk space saving is worth the small sacrifice in certainty, they are a very popular choice for set compression.

The base data structure of a Bloom filter is a bit vector—a sequence of cells that may contain 1 or 0 (or true or false). The level of compression (and the corresponding increase in uncertainty...