Exploratory Data Mining and Data Cleaning

Description

* Written for practitioners of data mining, data cleaning and database management.
* Presents a technical treatment of data quality including process, metrics, tools and algorithms.
* Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge.
* Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches.
* Uses case studies to illustrate applications in real life scenarios.
* Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques.

Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.

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1. Ebooks list page : 2674
2. Glenn J. Myatt, ?Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining?
3. Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining
4. Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining
5. Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining
6. Glenn J. Myatt, Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mini
7. Making Sense of Data II: A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications
8. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
9. Data Preparation for Data Mining (The Morgan Kaufmann Series in Data Management Systems)
10. Multimedia Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
11. Data Mining and Knowledge Discovery Technologies (Advances in Data Warehousing and Mining)
12. [request_ebook] The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming & Delivering Data
13. Exploratory Data Analysis with MATLAB (Computer Science and Data Analysis)
14. Exploratory Data Analysis with MATLAB (Computer Science and Data Analysis)
15. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data | Ebook Share
16. Organizational Data Mining: Leveraging Enterprise Data Resources for Optimal Performance