
By Eric Rochester
ISBN-10: 178216264X
ISBN-13: 9781782162643
Utilizing Clojure for information research and assortment, this booklet will enable you achieve clean insights and views out of your info with a vital choice of sensible, based recipes.
«The Clojure info research Cookbook» offers recipes for each degree of the information research strategy. no matter if scraping facts off an online web page, acting facts mining, or developing graphs for the net, this ebook has whatever for the duty at hand.
Youll the right way to collect information, fresh it up, and rework it into important graphs that may then be analyzed and released to the net. assurance contains complicated subject matters like processing facts at the same time, making use of robust statistical innovations like Bayesian modelling, or even info mining algorithms similar to K-means clustering, neural networks, and organization principles.
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Additional info for Clojure Data Analysis Cookbook
Example text
Html. It summarizes the syntax of Java's style of regular expressions. oracle. com/javase/tutorial/essential/regex/. html. But the REPL is usually what I use to build and test regular expressions. See also... " Now they have two problems. Regular expressions are a complex, dense, and often fiddly tool. Sometimes they are the right tool, but sometimes not. We'll see a more powerful, and often better, solution in the Parsing custom data formats recipe. Maintaining consistency with synonym maps One common problem with data is inconsistency.
If you have two items of data, what fields do you have to look at to determine if they are duplicates? And then, how close do they need to be? For this recipe, we'll examine some data and decide on duplicates by doing a fuzzy comparison of the name fields. We'll simply return all pairs that appear to be duplicates. 0-SNAPSHOT"]] And make sure that's available to our script or REPL. core) How to do it… 1. We'll first define a function to test for fuzzy equality. Then, we'll write another function that uses fuzzy equality to test whether two records match.
Fortunately, linked data's flexible and dynamic data model facilitates integrating data from multiple sources. For this recipe, we'll combine several previous ones. com) to get information out of a table, just as we did in the Scraping data from tables in web pages recipe. Finally, we'll dump everything into a triple store and pull it back out, as we did in the last recipe. ttl, which we've been using since the Reading RDF data recipe. How to do it… Since this is a longer recipe, we'll build it up in segments.
Clojure Data Analysis Cookbook by Eric Rochester
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