Witten, Data Mining, Practical Machine Learning Tools and Techniques, pg 60
There is no substitute for getting to know your data. Simple tools that show histograms of the distribution of values of nominal attributes, and graphs of the values of numeric attributes (perhaps sorted or simply graphed against instance number), are very helpful. These graphical visualizations of the data make it easy to identify outliers, which may well represent errors in the data file—or arcane conventions for coding unusual situations, such as a missing year as 9999 or a missing weight as -1 kg, that no one has thought to tell you about. Domain experts need to be consulted to explain anomalies, missing values, the significance of integers that represent categories rather than numeric quantities, and so on. Pairwise plots of one attribute against another, or each attribute against the class value, can be extremely revealing.
Data cleaning is a time-consuming and labor-intensive procedure but one that is absolutely necessary for successful data mining. With a large dataset, people often give up—how can they possibly check it all? Instead, you should sample a few instances and examine them carefully. You’ll be surprised at what you find. Time looking at your data is always well spent.
There is no substitute for getting to know your data. Simple tools that show histograms of the distribution of values of nominal attributes, and graphs of the values of numeric attributes (perhaps sorted or simply graphed against instance number), are very helpful. These graphical visualizations of the data make it easy to identify outliers, which may well represent errors in the data file—or arcane conventions for coding unusual situations, such as a missing year as 9999 or a missing weight as -1 kg, that no one has thought to tell you about. Domain experts need to be consulted to explain anomalies, missing values, the significance of integers that represent categories rather than numeric quantities, and so on. Pairwise plots of one attribute against another, or each attribute against the class value, can be extremely revealing.
Data cleaning is a time-consuming and labor-intensive procedure but one that is absolutely necessary for successful data mining. With a large dataset, people often give up—how can they possibly check it all? Instead, you should sample a few instances and examine them carefully. You’ll be surprised at what you find. Time looking at your data is always well spent.
