Exploratory Data Mining and Data Cleaning

Tamraparni Dasu, Theodore Johnson

  • 出版商: Wiley
  • 出版日期: 2003-06-10
  • 售價: $1,180
  • 貴賓價: 9.8$1,156
  • 語言: 英文
  • 頁數: 224
  • 裝訂: Hardcover
  • ISBN: 0471268518
  • ISBN-13: 9780471268512
  • 相關分類: Data-mining
  • 下單後立即進貨 (約5~7天)



A unique, integrated approach to exploratory data mining and data quality

Data analysts at information-intensive businesses are frequently asked to analyze new data sets that are often dirty–composed of numerous tables possessing unknown properties. Prior to analysis, this data must be cleaned and explored–often a long and arduous task. Ensuring data quality is a notoriously messy problem that can only be addressed by drawing on methods from many disciplines, including statistics, exploratory data mining, database management, and metadata coding.

Where other books on data mining and analysis focus primarily on the last stage of the analysis procedure, Exploratory Data Mining and Data Cleaning uses a uniquely integrated approach to data exploration and data cleaning to develop a suitable modeling strategy that will help analysts to more effectively determine and implement the final technique.

The authors, both seasoned data analysts at a major corporation, draw on their own professional experience to:

  • Present a brief overview of the main analytical techniques used in data mining practices, such as univariate and multivariate summaries of attributes and their interactions including Q -Q plots, fractal dimension and histograms, nonparametric approaches incorporating data depth, and more
  • Provide numerous references to the related literature on clustering, classification, regression, and more
  • Focus on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge
  • Address methods of detecting, quantifying (metrics), and correcting data quality issues that significantly impact findings and decisions, using commercially available tools as well as new algorithmic approaches
  • Use case studies to illustrate applications in real-life scenarios
  • Highlight new approaches and methodologies, such as the DataSphere space partitioning and summary-based analysis techniques

A groundbreaking addition to the existing literature, Exploratory Data Mining and Data Cleaning serves as an important reference for data analysts who need to analyze large amounts of unfamiliar data, operations managers, and students in undergraduate or graduate-level courses dealing with data analysis and data mining.

Table of Contents

0 Preface.

1 Exploratory Data Mining and Data Cleaning: An Overview.

2 Exploratory Data Mining.

3 Partitions and Piecewise Models.

4 Data Quality.  

5 Data Quality: Techniques and Algorithms.






  • 簡要介紹數據挖掘實踐中主要的分析技術,例如單變量和多變量屬性的摘要以及它們的相互作用,包括Q-Q圖、分形維度和直方圖,包含數據深度的非參數方法等

  • 提供大量關於聚類、分類、回歸等相關文獻的參考資料

  • 著重於通過迭代的數據探索循環和領域知識的融入來發展一種不斷演進的建模策略

  • 介紹檢測、量化(指標)和修正嚴重影響結果和決策的數據質量問題的方法,包括商業可用工具和新的算法方法

  • 使用案例研究來說明在實際情境中的應用

  • 突出新的方法和方法,例如DataSphere空間劃分和基於摘要的分析技術



0 前言。

1 探索性數據挖掘和數據清理:概述。

2 探索性數據挖掘。

3 分區和分段模型。

4 數據質量。

5 數據質量:技術和算法。