Incomplete Missing) Data Data is not always available E.g. many tuples have no recorded value for several attributes such as customer income in sales data Missing data may be due to equipment malfunction inconsistent with other recorded data and thus deleted data not entered due to misunderstanding certain data may not be considered important at the time of entry not register history or changes of the data Missing data may need to be inferred
7 Incomplete (Missing) Data ◼ Data is not always available ◼ E.g., many tuples have no recorded value for several attributes, such as customer income in sales data ◼ Missing data may be due to ◼ equipment malfunction ◼ inconsistent with other recorded data and thus deleted ◼ data not entered due to misunderstanding ◼ certain data may not be considered important at the time of entry ◼ not register history or changes of the data ◼ Missing data may need to be inferred
How to Handle missing Data? ignore the tuple: usually done when class label is missing (when doing classification-not effective when the % of missing values per attribute varies considerably Fill in the missing value manually: tedious infeasible? Fill in it automatically with a global constant e.g. ,"unknown",a new class the attribute mean the attribute mean for all samples belonging to the same class: smarter the most probable value: inference-based such as Bayesian formula or decision tree
8 How to Handle Missing Data? ◼ Ignore the tuple: usually done when class label is missing (when doing classification)—not effective when the % of missing values per attribute varies considerably ◼ Fill in the missing value manually: tedious + infeasible? ◼ Fill in it automatically with ◼ a global constant : e.g., “unknown”, a new class?! ◼ the attribute mean ◼ the attribute mean for all samples belonging to the same class: smarter ◼ the most probable value: inference-based such as Bayesian formula or decision tree
Noisy Data Noise: random error or variance in a measured variable Incorrect attribute values may be due to faulty data collection instruments data entry problems data transmission problems technology limitation inconsistency in naming convention Other data problems which require data cleaning duplicate records incomplete data inconsistent data
9 Noisy Data ◼ Noise: random error or variance in a measured variable ◼ Incorrect attribute values may be due to ◼ faulty data collection instruments ◼ data entry problems ◼ data transmission problems ◼ technology limitation ◼ inconsistency in naming convention ◼ Other data problems which require data cleaning ◼ duplicate records ◼ incomplete data ◼ inconsistent data
How to Handle noisy Data? Binning first sort data and partition into equal-frequency bins then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc. Regression smooth by fitting the data into regression functions Clustering detect and remove outliers Combined computer and human inspection detect suspicious values and check by human( e. g deal with possible outliers 10
10 How to Handle Noisy Data? ◼ Binning ◼ first sort data and partition into (equal-frequency) bins ◼ then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc. ◼ Regression ◼ smooth by fitting the data into regression functions ◼ Clustering ◼ detect and remove outliers ◼ Combined computer and human inspection ◼ detect suspicious values and check by human (e.g., deal with possible outliers)
Data Cleaning as a Process Data discrepancy detection Use metadata(e. g, domain, range, dependency distribution) Check field overloading Check uniqueness rule consecutive rule and null rule Use commercial tools Data scrubbing use simple domain knowledge( e.g. post code, spell-check to detect errors and make corrections Data auditing by analyzing data to discover rules and relationship to detect violators(e.g, correlation and clustering to find outliers) Data migration and integration Data migration tools: allow transformations to be specified ETL(EXtraction/Transformation/Loading)tools: allow users to specify transformations through a graphical user interface Integration of the two processes Iterative and interactive(e.g. Potter's Wheels) 11
11 Data Cleaning as a Process ◼ Data discrepancy detection ◼ Use metadata (e.g., domain, range, dependency, distribution) ◼ Check field overloading ◼ Check uniqueness rule, consecutive rule and null rule ◼ Use commercial tools ◼ Data scrubbing: use simple domain knowledge (e.g., postal code, spell-check) to detect errors and make corrections ◼ Data auditing: by analyzing data to discover rules and relationship to detect violators (e.g., correlation and clustering to find outliers) ◼ Data migration and integration ◼ Data migration tools: allow transformations to be specified ◼ ETL (Extraction/Transformation/Loading) tools: allow users to specify transformations through a graphical user interface ◼ Integration of the two processes ◼ Iterative and interactive (e.g., Potter’s Wheels)