
















































Study with the several resources on Docsity
Earn points by helping other students or get them with a premium plan
Prepare for your exams
Study with the several resources on Docsity
Earn points to download
Earn points by helping other students or get them with a premium plan
Community
Ask the community for help and clear up your study doubts
Discover the best universities in your country according to Docsity users
Free resources
Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors
Data Preprocessing slides with all the stages covered.
Typology: Lecture notes
1 / 56
This page cannot be seen from the preview
Don't miss anything!
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
How to Handle Noisy Data?
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)
Data Integration (^) Data integration : (^) Combines data from multiple sources into a coherent store (^) Schema integration: e.g., A.cust-id B.cust-# (^) Integrate metadata from different sources (^) Entity identification problem: (^) Identify real world entities from multiple data sources, e.g., Bill Clinton = William Clinton (^) Detecting and resolving data value conflicts For the same real world entity, attribute values from different sources are different (^) Possible reasons: different representations, different scales, e.g., metric vs. British units
Redundant data occur often when integration of multiple databases Object identification : The same attribute or object may have different names in different databases Derivable data: One attribute may be a “derived” attribute in another table, e.g., annual revenue (^) Redundant attributes may be able to be detected by correlation analysis and covariance analysis Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies and improve mining speed and quality
where n is the number of tuples, and are the respective means of A and B, σA and σB are the respective standard deviation of A and B, and Σ(aibi) is the sum of the AB cross- product.
A B n i i i A B n i i i A B n ab nA B n a A b B r ( 1 ) ( ) ( 1 ) ( )( ) 1 1 ,
Co-Variance: An Example (^) It can be simplified in computation as (^) Suppose two stocks A and B have the following values in one week: (2, 5), (3, 8), (5, 10), (4, 11), (6, 14). (^) Question: If the stocks are affected by the same industry trends, will their prices rise or fall together? (^) E(A) = (2 + 3 + 5 + 4 + 6)/ 5 = 20/5 = 4 E(B) = (5 + 8 + 10 + 11 + 14) /5 = 48/5 = 9. (^) Cov(A,B) = (2×5+3×8+5×10+4×11+6×14)/5 − 4 × 9.6 = 4 (^) Thus, A and B rise together since Cov(A, B) > 0.