Categorical and continuous variables can be divided into nominal, binary, ordinal, integer, interval-scaled and ratio-scaled types. Data cleaning involves detecting and removing noisy, invalid and outlier values that are incorrectly recorded or outside normal ranges. Missing values can occur due to equipment malfunctions, added fields, or unavailable information, and can be dealt with by discarding instances or replacing values with averages or most frequent values.

1. DATA FOR DATA MINING

2. Types of variablesCan be divided into two main types: Categorical attributes - Nominal, binary and ordinal variablesContinuous attributes– integer, interval-scaled and ratio-scaled variablesIgnore attribute (optional) - Variables which are of no significance

3. Data CleaningErroneous values can be divided into:Noisy value: Valid for the dataset, but incorrectly recordedInvalid values: Can be easily detected and removed/correctedNoise detection:Peaks in the datasetSome values outside the normal range: Such values could even be genuine (called as Outliers)

4. Missing ValuesReasons of occurrence: Equipment malfunctionAdditional fields were added laterNon-availability of information Strategies to deal with missing valuesDiscard instancesReplace by most frequent/average value

5. Visit more self help tutorialsPick a tutorial of your choice and browse through it at your own pace.The tutorials section is free, self-guiding and will not involve any additional support.Visit us at www.dataminingtools.net