How do you identify common data quality issues in data mining?

3 Duplicate values

A third common data quality issue in data mining is duplicate values. Duplicate values occur when some data entries are repeated or identical in the data. Duplicate values can result from data integration, such as merging or joining different data sources, or from data collection, such as multiple records or submissions. Duplicate values can affect the quality of data mining results, as they inflate the size of the data and skew the distribution and analysis of the data. To identify duplicate values, you can use data deduplication techniques, such as hashing, matching, or clustering, to compare and group similar or identical data entries. You can also use data cleansing tools, such as sorting, filtering, or removing, to eliminate or reduce duplicate data entries.

4 Outlier values

A fourth common data quality issue in data mining is outlier values. Outlier values are data entries that are significantly different from the rest of the data. Outlier values can result from data variability, such as natural variation or measurement error, or from data anomalies, such as errors, fraud, or outliers. Outlier values can affect the quality of data mining results, as they distort the distribution and analysis of the data and may indicate potential problems or opportunities. To identify outlier values, you can use outlier detection methods, such as statistical tests, distance measures, or density measures, to identify and quantify the deviation of data entries from the normal or expected range. You can also use outlier treatment techniques, such as trimming, winsorizing, or imputing, to modify or replace outlier values with more reasonable values.

5 Irrelevant values

A fifth common data quality issue in data mining is irrelevant values. Irrelevant values are data entries that are not related or useful for the data mining objective or task. Irrelevant values can result from data selection, such as including or excluding certain variables or observations, or from data transformation, such as aggregating or discretizing data. Irrelevant values can affect the quality of data mining results, as they reduce the efficiency and effectiveness of data mining algorithms and may introduce noise or bias. To identify irrelevant values, you can use feature selection methods, such as filter, wrapper, or embedded methods, to evaluate and select the most relevant and informative variables for the data mining task. You can also use dimensionality reduction techniques, such as principal component analysis, factor analysis, or singular value decomposition, to reduce the number of variables and retain the most important information in the data.