Mastering Data Mining with Python by Megan Squire

By Megan Squire

Key Features

  • Dive deeper into info mining with Python – do not be complacent, sharpen your skills!
  • From the commonest parts of knowledge mining to state of the art thoughts, now we have you coated for any data-related challenge
  • Become a extra fluent and assured Python data-analyst, in complete regulate of its broad variety of libraries

Book Description

Data mining is a vital part of the information technology pipeline. it's the origin of any winning data-driven approach – with out it, you will by no means be ready to discover actually transformative insights. considering the fact that facts is key to nearly each sleek association, it truly is worthy taking the next move to unencumber even higher worth and extra significant understanding.

If the basics of information mining with Python, you're now able to scan with extra attention-grabbing, complicated info analytics ideas utilizing Python's easy-to-use interface and large variety of libraries.

In this publication, you will move deeper into many usually ignored components of information mining, together with organization rule mining, entity matching, community mining, sentiment research, named entity reputation, textual content summarization, subject modeling, and anomaly detection. for every information mining process, we will assessment the cutting-edge and present most sensible practices ahead of evaluating a large choice of recommendations for fixing each one challenge. we are going to then enforce instance ideas utilizing real-world info from the area of software program engineering, and we'll spend time studying easy methods to comprehend and interpret the consequences we get.

By the tip of this e-book, you may have reliable event enforcing essentially the most attention-grabbing and proper info mining concepts on hand this present day, and you'll have accomplished a better fluency within the very important box of Python information analytics.

What you are going to learn

  • Explore options for locating widespread itemsets and organization ideas in huge facts sets
  • Learn id tools for entity suits throughout many differing types of data
  • Identify the fundamentals of community mining and the way to use it to real-world information sets
  • Discover tools for detecting the sentiment of textual content and for finding named entities in text
  • Observe a number of strategies for immediately extracting summaries and producing subject types for text
  • See tips to use information mining to mend information anomalies and the way to exploit computing device studying to spot outliers in an information set

About the Author

Megan Squire is a professor of computing sciences at Elon University.

Her basic examine curiosity is in amassing, cleansing, and reading information approximately how loose and open resource software program is made. She is likely one of the leaders of the FLOSSmole.org, FLOSSdata.org, and FLOSSpapers.org projects.

Table of Contents

  1. Expanding Your information Mining Toolbox
  2. Association Rule Mining
  3. Entity Matching
  4. Network Analysis
  5. Sentiment research in Text
  6. Named Entity popularity in Text
  7. Automatic textual content Summarization
  8. Topic Modeling in Text
  9. Mining for info Anomalies

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Sample text

Finding frequent itemsets is a type of counting activity. But unlike producing a simple tally of items we observe in a dataset (today we sold 80 carrots and 100 tomatoes), finding frequent itemsets is slightly different. Specifically, to find frequent itemsets we look for co-occurring sets of items within some larger group. These larger groups are sometimes imagined as supermarket transactions or shopping baskets, and the entire exercise is sometimes called market basket analysis. Staying with the supermarket analogy, the items co-occurring within those baskets are sometimes imagined to be combinations of products purchased at the supermarket.

A project – discovering association rules in software project tags In 1997, the website, Freshmeat, was created as a directory that tracked free, libre, and open source software (FLOSS) projects. In 2011, the site was renamed Freecode. After sales and acquisitions and several site redesigns, in 2014 all updates to the Freecode site were discontinued. The site remains online, but it is no longer being updated and no new projects are being added to the directory. Freecode now serves as a snapshot of facts about FLOSS projects during the late 1990s and 2000s.

After that, we are simply calculating based on previously found counts. An important principle that will help us find frequent itemsets faster is called the upward closure property. Upward closure states that an itemset can only be frequent if all the items in it are also frequent. In other words, there is no sense in calculating the support for any itemset if all the itemsets contained in it are not also frequent. [ 28 ] Chapter 2 Why is it important to know about closure? Because knowing this rule will save us a lot of time in calculating the possible itemsets.

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