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What Is Data Mining? Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Alternative names Data mining: a misnomer? Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. Watch out: Is everything “data mining”? Simple search and query processing (Deductive) expert systems May 22, 2017 Data Mining: Concepts and Techniques 1 Multi-Dimensional View of Data Mining Data to be mined Knowledge to be mined Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc. Multiple/integrated functions and mining at multiple levels Techniques utilized Relational, data warehouse, transactional, stream, objectoriented/relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, etc. Applications adapted May 22, 2017 Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc. Data Mining: Concepts and Techniques 2 Major Issues in Data Mining Mining methodology Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web Performance: efficiency, effectiveness, and scalability Pattern evaluation: the interestingness problem Incorporation of background knowledge Handling noise and incomplete data Parallel, distributed and incremental mining methods Integration of the discovered knowledge with existing one: knowledge fusion User interaction Data mining query languages and ad-hoc mining Expression and visualization of data mining results Interactive mining of knowledge at multiple levels of abstraction Applications and social impacts May 22, 2017 Domain-specific data mining & invisible data mining Protection of data security, integrity, and privacy Data Mining: Concepts and Techniques 3 Chapter 2: Data Preprocessing Why preprocess the data? Descriptive data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary May 22, 2017 Data Mining: Concepts and Techniques 4 Why Data Preprocessing? Data in the real world is dirty incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data noisy: containing errors or outliers e.g., Salary=“-10” inconsistent: containing discrepancies in codes or names May 22, 2017 e.g., occupation=“ ” 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 Data Mining: Concepts and Techniques 5 Why Is Data Dirty? Incomplete data may come from 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 “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 Different data sources Functional dependency violation (e.g., modify some linked data) Duplicate records also need data cleaning May 22, 2017 Data Mining: Concepts and Techniques 6 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 May 22, 2017 Data Mining: Concepts and Techniques 7 Multi-Dimensional Measure of Data Quality A well-accepted multidimensional view: Accuracy Completeness Consistency Timeliness Believability Value added Interpretability Accessibility Broad categories: Intrinsic, contextual, representational, and accessibility May 22, 2017 Data Mining: Concepts and Techniques 8 Major Tasks in Data Preprocessing Data cleaning Data integration Normalization and aggregation Data reduction Integration of multiple databases, data cubes, or files Data transformation Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies Obtains reduced representation in volume but produces the same or similar analytical results Data discretization Part of data reduction but with particular importance, especially for numerical data May 22, 2017 Data Mining: Concepts and Techniques 9 Forms of Data Preprocessing May 22, 2017 Data Mining: Concepts and Techniques 10 Chapter 2: Data Preprocessing Why preprocess the data? Descriptive data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary May 22, 2017 Data Mining: Concepts and Techniques 11 Mining Data Descriptive Characteristics Motivation Data dispersion characteristics To better understand the data: central tendency, variation and spread median, max, min, quantiles, outliers, variance, etc. Numerical dimensions correspond to sorted intervals Data dispersion: analyzed with multiple granularities of precision Boxplot or quantile analysis on sorted intervals Dispersion analysis on computed measures Folding measures into numerical dimensions Boxplot or quantile analysis on the transformed cube May 22, 2017 Data Mining: Concepts and Techniques 12 Measuring the Central Tendency 1 n Mean (algebraic measure) (sample vs. population): x xi n i 1 Weighted arithmetic mean: x N n Trimmed mean: chopping extreme values x Median: A holistic measure w x i 1 n i i w i 1 i Middle value if odd number of values, or average of the middle two values otherwise Estimated by interpolation (for grouped data): median L1 ( Mode Value that occurs most frequently in the data Unimodal, bimodal, trimodal Empirical formula: May 22, 2017 n / 2 ( f )l f median )c mean mode 3 (mean median) Data Mining: Concepts and Techniques 13 Measuring the Dispersion of Data Quartiles, outliers and boxplots Quartiles: Q1 (25th percentile), Q3 (75th percentile) Inter-quartile range: IQR = Q3 – Q1 Five number summary: min, Q1, M, Q3, max Boxplot: ends of the box are the quartiles, median is marked, whiskers, and plot outlier individually Outlier: usually, a value higher/lower than 1.5 x IQR Variance and standard deviation (sample: s, population: σ) Variance: (algebraic, scalable computation) 1 n 1 n 2 1 n 2 s ( xi x ) [ xi ( xi ) 2 ] n 1 i 1 n 1 i 1 n i 1 2 1 N 2 n 1 ( x ) i N i 1 2 n xi 2 2 i 1 Standard deviation s (or σ) is the square root of variance s2 (or σ2) May 22, 2017 Data Mining: Concepts and Techniques 14

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