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Haskell Data Analysis cookbook

You're reading from   Haskell Data Analysis cookbook Explore intuitive data analysis techniques and powerful machine learning methods using over 130 practical recipes

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Product type Paperback
Published in Jun 2014
Publisher
ISBN-13 9781783286331
Length 334 pages
Edition 1st Edition
Languages
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Author (1):
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Nishant Shukla Nishant Shukla
Author Profile Icon Nishant Shukla
Nishant Shukla
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Toc

Table of Contents (14) Chapters Close

Preface 1. The Hunt for Data FREE CHAPTER 2. Integrity and Inspection 3. The Science of Words 4. Data Hashing 5. The Dance with Trees 6. Graph Fundamentals 7. Statistics and Analysis 8. Clustering and Classification 9. Parallel and Concurrent Design 10. Real-time Data 11. Visualizing Data 12. Exporting and Presenting Index

Finding the longest common subsequence


One way to compare string similarity is by finding their longest common subsequence. This is useful in finding differences between mutations of data such as source code or genome sequences.

A subsequence of a string is the same string with zero or more of the indices removed. So, some possible subsequences of "BITCOIN" could be "ITCOIN", "TON", "BIN", or even "BITCOIN" itself, as shown in the following figure:

The longest common subsequence is exactly what it sounds like. It is the longest subsequence common to both strings. For example, the longest common subsequence of "find the lights" and "there are four lights" is "the lights."

Getting ready

Install the data-memocombinators package from Cabal. This allows us to minimize redundant computations to improve runtime as follows:

$ cabal install data-memocombinators

How to do it...

  1. The only import we will need is this handy package to easily support memoization:

    import qualified Data.MemoCombinators as Memo...
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