Bài giảng khai phá dữ liệu (data mining) data preprocessing trịnh tấn đạt

Trịnh Tấn Đạt Khoa CNTT – Đại Học Sài Gòn Email: trinhtandat@sgu.edu.vn Website: https://sites.google.com/site/ttdat88/ Outline  Why preprocess the data?  Descriptive data summarization  Data cleaning  Data integration and transformation  Data reduction  Discretization and concept hierarchy generation  Summary Why Data Preprocessing?  Data in the real world is dirty  incomplete: lacking attribute values, lacking certain attributes of interest, …  e.g., occupation=“ ”  noisy: containing errors or outliers  e.g., Salary=“-10”  inconsistent: containing discrepancies in codes or names  e.g., Age=“42” Birthday=“03/07/1997”  e.g., Was rating “1,2,3”, now rating “A, B, C”  e.g., discrepancy between duplicate records Why Is Data Dirty?  Incomplete data may come from  “Not applicable” data value when collected  Different considerations between the time when the data was collected and when it is analyzed  Human/hardware/software problems  Noisy data (incorrect values) may come from  Faulty data collection instruments  Human or computer error at data entry  Errors in data transmission  Inconsistent data may come from  Different data sources  Functional dependency violation (e.g., modify some linked data)  Duplicate records also need data cleaning Why Is Data Preprocessing Important?  No quality data, no quality mining results!  Quality decisions must be based on quality data  e.g., duplicate or missing data may cause incorrect or even misleading statistics  Data warehouse needs consistent integration of quality data  Data extraction, cleaning, and transformation comprises the majority of the work of building a data warehouse Multi-Dimensional Measure of Data Quality  A well-accepted multidimensional view:  Accuracy  Completeness  Consistency  Timeliness  Believability  Value added  Interpretability  Accessibility Data type  Numeric: The most used data type, and the stored content is numeric  Characters and strings: strings are arrays of characters  Boolean: for binary data with true and false values  Time series data: including time-or sequential-related properties  Sequential data: data itself has sequential relationship  Time series data: each data will be subject to change with time Data type  Spatial data: for data including special related attributes  For example, Google Map, Integrated Circuit Design Layout, Wafer Exposure Layout, Global Positioning System (GPS), etc  Text data: for paragraph description, including patent reports, diagnostic reports, etc  Structured data: library bibliographic data, credit card data  Semi-structured data: email, extensible markup language (XML)  Unstructured data: social media data of messages in Facebook  Multimedia data: Including data of pictures, audio, video, etc in media with mass data volumes as compared to other types of data that need data compression for data storage Data scale “A proxy attribute is a variable that is used to represent or stand in for another variable or attribute that is difficult to measure directly A proxy attribute is typically used in situations where it is not possible or practical to measure the actual attribute of interest For example, in a study of income, the amount of money a person earns per year may be difficult to determine accurately In such a case, a proxy attribute, such as education level or occupation, may be used instead.” ChatGPT  Each variable of data has its corresponding attribute and scale to quantify and measure its level  natural quantitative scale  qualitative scale  When one variable is hard to find the corresponding attribute, proxy attribute can be used instead as a measurement  Common scales: nominal scale, categorical scale, ordinal scale, interval scale, ratio scale, and absolute scale Six common scales  nominal scale: only used as codes, where the values has no meaning for      mathematical operations categorical scale: according to its characteristics, and each category is marked with a numeric code to indicate the category to which it belongs ordinal scale: to express the ranking and ordering of the data without establishing the degree of variation between them interval scale: also called distance scale, can describes numerical differences between different numbers in a meaningful way ratio scale: different numbers can be compared to each other by ratio absolute scale: the numbers measured have absolute meaning 10