Mastering Data Mining with Python

Megan Squire

Mastering Data Mining with Python

商品描述

Key Features

  • Dive deeper into data mining with Python don't be complacent, sharpen your skills!
  • From the most common elements of data mining to cutting-edge techniques, we've got you covered for any data-related challenge
  • Become a more fluent and confident Python data-analyst, in full control of its extensive range of libraries

Book Description

Data mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding.

If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries.

In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get.

By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics.

What you will learn

  • Explore techniques for finding frequent itemsets and association rules in large data sets
  • Learn identification methods for entity matches across many different types of data
  • Identify the basics of network mining and how to apply it to real-world data sets
  • Discover methods for detecting the sentiment of text and for locating named entities in text
  • Observe multiple techniques for automatically extracting summaries and generating topic models for text
  • See how to use data mining to fix data anomalies and how to use machine learning to identify outliers in a data set

About the Author

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

Her primary research interest is in collecting, cleaning, and analyzing data about how free and open source software is made. She is one of the leaders of the FLOSSmole.org, FLOSSdata.org, and FLOSSpapers.org projects.

Table of Contents

  1. Expanding Your Data Mining Toolbox
  2. Association Rule Mining
  3. Entity Matching
  4. Network Analysis
  5. Sentiment Analysis in Text
  6. Named Entity Recognition in Text
  7. Automatic Text Summarization
  8. Topic Modeling in Text
  9. Mining for Data Anomalies

商品描述(中文翻譯)

主要特點


  • 深入探索使用Python進行數據挖掘，不要滿足於現狀，提升你的技能！

  • 從數據挖掘的常見元素到尖端技術，我們為你提供了應對任何數據相關挑戰的解決方案

  • 成為一名更流利和自信的Python數據分析師，完全掌握其廣泛的庫範圍

書籍描述

數據挖掘是數據科學流程的重要組成部分。它是任何成功的數據驅動策略的基礎，沒有它，你將無法揭示真正具有轉型意義的洞察力。由於數據對於現代組織來說至關重要，因此值得進一步釋放更大的價值和更有意義的理解。

如果你已經掌握了Python數據挖掘的基礎知識，現在你可以使用Python易於使用的界面和廣泛的庫範圍來嘗試更有趣、更高級的數據分析技術。

在本書中，你將深入研究數據挖掘中常常被忽視的許多領域，包括關聯規則挖掘、實體匹配、網絡挖掘、情感分析、命名實體識別、文本摘要、主題建模和異常檢測。對於每種數據挖掘技術，我們將回顧最新技術和當前最佳實踐，然後比較各種解決每個問題的策略。然後，我們將使用軟件工程領域的真實數據實施示例解決方案，並花時間學習如何理解和解釋我們得到的結果。

通過閱讀本書，你將獲得實施當今最有趣和相關的數據挖掘技術的豐富經驗，並在Python數據分析這一重要領域中取得更高的流利度。

你將學到什麼


  • 探索在大數據集中尋找頻繁項集和關聯規則的技術

  • 學習跨多種不同類型數據的實體匹配方法

  • 了解網絡挖掘的基礎知識以及如何應用於真實數據集

  • 發現文本情感分析和命名實體識別的方法

  • 觀察多種自動提取摘要和生成文本主題模型的技術

  • 了解如何使用數據挖掘修復數據異常，以及如何使用機器學習識別數據集中的異常值

關於作者

Megan Squire是Elon大學的計算科學教授。

她的主要研究興趣是收集、清理和分析有關自由和開源軟件製作方式的數據。她是FLOSSmole.org、FLOSSdata.org和FLOSSpapers.org項目的領導者之一。

目錄


  1. 擴展你的數據挖掘工具箱

  2. 關聯規則挖掘

  3. 實體匹配

  4. 網絡分析

  5. 文本情感分析

  6. 文本命名實體識別

  7. 自動文本摘要

  8. 文本主題建模

  9. 數據異常挖掘

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