Steven F. Ashby Center for Applied Scientific Computing
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Transcript Steven F. Ashby Center for Applied Scientific Computing
Data Mining: Data
Lecture 3
TIES445 Data mining
Nov-Dec 2007
Sami Äyrämö
These slides are additional material for TIES445
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Data quality
GIGO – Garbage In, Garbage Out
– Effectiveness of DM exercise depends on the quality
of data
Data quality concerns
– individual measurements (records and fields)
– collections of observations
Sources of error are infinite
– Human error (e.g., keyboard error)
– Instrumentation failure
Inaccuare or imprecise
– Inadequate specification of measurement or data
collection process
These slides are additional material for TIES445
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Quality of individual measurements
Bias
– the difference between the mean of the repeated
measurements and the true value
Precision
– variability of the repeated measurements (NOTE: precision
is not the number of digits in record)
Accuracy
– small bias and high precision (e.g., small variance)
– e.g, repeated measurement of someone’s height may be
precise (reliable), but inaccurate (validity), if (s)he is
wearing shoes (we are not measuring the right thing)
True value (does it even exist?)
These slides are additional material for TIES445
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Quality of collections of data : bias
Distorted (biased) samples
– mismatch between the sample population and and the population
of interest (selection bias)
e.g., calculating an average age of students in Jyväskylä when the
sample is restricted to female students
– a sample may be selected through a chain of selection steps
e.g., candidates for bank loans: 1) potential customers are contacted,
2) some reply, some do not, 3) of those who replied some are
creditworthy, some are not, 4) those who take out a loan are followed,
5) some are good customers, some are not,…
– populations are not static (population drift)
e.g., customers shopping behaviour may change over time
A biased sample leads to inconsistent estimates of population
parameters
These slides are additional material for TIES445
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Quality of collections of data:
Incomplete data
Incomplete data: missing or empty values
– Missing value: Information is not collected
e.g., People decline to answer a question (age, weight, position,…)
– Empty value: Information does not exist
A form may have conditional parts: e.g., expiry date of an driver’s
license can not be filled out by children
– Determining whether any value is ”empty” or ”missing”
requires domain knowledge
If the discriminating information is not provided both empty and
missing values are treated as ”and called” missing
– Fundamental question for data mining task: ”Why are the
data incomplete?”
– Note: A distorted (biased) sample is actually a special case
of incomplete data
These slides are additional material for TIES445
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