Data Mining: Concepts and Techniques What is Data? Attributes Collection of data objects and their attributes An attribute is a property or characteristic of an object Examples: eye color of a person, temperature, etc. Attribute is also known as variable, field, characteristic, or feature A collection of attributes describe an object Object is also known as record, point, case, sample, entity, or instance Objects April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Transaction Data Market-Basket transactions April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Data Matrix [ 1 4 5 6 2 3 3 4 2 2 1 1 ] April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques % 1. Title: Iris Plants Database % % 2. Sources: % (a) Creator: R.A. Fisher % (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) % (c) Date: July, 1988 % @RELATION iris @ATTRIBUTE sepallength NUMERIC @ATTRIBUTE sepalwidth NUMERIC @ATTRIBUTE petallength NUMERIC @ATTRIBUTE petalwidth NUMERIC @ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica} The Data of the ARFF file looks like the following: @DATA 5.1,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa 4.7,3.2,1.3,0.2,Iris-setosa April 16, 2017 Data Mining: Concepts and Techniques

uci machine learning repository http://mlearn.ics.uci.edu/databases/ April 16, 2017 Data Mining: Concepts and Techniques

uci machine learning repository http://archive.ics.uci.edu/ml/datasets.html/ April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Attribute Values Attribute values are numbers or symbols assigned to an attribute Distinction between attributes and attribute values Same attribute can be mapped to different attribute values Example: height can be measured in feet or meters Different attributes can be mapped to the same set of values Example: Attribute values for ID and age are integers But properties of attribute values can be different ID has no limit but age has a maximum and minimum value April 16, 2017 Data Mining: Concepts and Techniques

Discrete and Continuous Attributes Discrete Attribute (Categorical Attribute) Has only a finite or countably infinite set of values Examples: zip codes, counts, or the set of words in a collection of documents Often represented as integer variables. Note: binary attributes are a special case of discrete attributes Continuous Attribute (Numerical Attribute) Has real numbers as attribute values Examples: temperature, height, or weight. Practically, real values can only be measured and represented using a finite number of digits. Continuous attributes are typically represented as floating-point variables. April 16, 2017 Data Mining: Concepts and Techniques

Discrete and Continuous Attributes April 16, 2017 Data Mining: Concepts and Techniques

Chapter 3: Data Preprocessing Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary April 16, 2017 Data Mining: Concepts and Techniques

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 inconsistent: containing discrepancies in codes or names No quality data, no quality mining results! Quality decisions must be based on quality data Data warehouse needs consistent integration of quality data April 16, 2017 Data Mining: Concepts and Techniques

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. April 16, 2017 Data Mining: Concepts and Techniques

Major Tasks in Data Preprocessing Data cleaning Fill in missing values, smooth noisy data, identify or remove outliers, and resolve inconsistencies Data integration Integration of multiple databases, data cubes, or files Data transformation Normalization and aggregation Data reduction 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 April 16, 2017 Data Mining: Concepts and Techniques

Chapter 3: Data Preprocessing Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Data Cleaning Data cleaning tasks Fill in missing values Identify outliers and smooth out noisy data Correct inconsistent data April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques 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. April 16, 2017 Data Mining: Concepts and Techniques

Imputing Values to Missing Data In federated data, between 30%-70% of the data points will have at least one missing attribute - data wastage if we ignore all records with a missing value Remaining data is seriously biased Lack of confidence in results Understanding pattern of missing data unearths data integrity issues If you are building a model, can’t use a record if an important attribute is missing. But fields might not be missing at random (actually probably not : income vs. missing telephone number). April 16, 2017 Data Mining: Concepts and Techniques

How to Handle Missing Data? Ignore the tuple: usually done when class label is missing (assuming the tasks in classification—not effective when the percentage of missing values per attribute varies considerably. Fill in the missing value manually: tedious + infeasible? Use a global constant to fill in the missing value: e.g., “unknown”, a new class?! Use the attribute mean to fill in the missing value Use the attribute mean for all samples belonging to the same class to fill in the missing value: smarter Use the most probable value to fill in the missing value: inference-based such as Bayesian formula or decision tree April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Noise Noise refers to modification of original values Examples: distortion of a person’s voice when talking on a poor phone and “snow” on television screen Two Sine Waves Two Sine Waves + Noise April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Outliers Outliers are data objects with characteristics that are considerably different than most of the other data objects in the data set April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Outliers Outliers are data objects with characteristics that are considerably different than most of the other data objects in the data set April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Noisy Data Noise: random error or variance in a measured variable Incorrect attribute values may due to faulty data collection instruments data entry problems data transmission problems technology limitation inconsistency in naming convention April 16, 2017 Data Mining: Concepts and Techniques

How to Handle Noisy Data? Binning method: first sort data and partition into (equi-depth) bins then one can smooth by bin means, smooth by bin median, smooth by bin boundaries, etc. Clustering detect and remove outliers Combined computer and human inspection detect suspicious values and check by human Regression smooth by fitting the data into regression functions April 16, 2017 Data Mining: Concepts and Techniques

Binning Methods for Data Smoothing * Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 34 * Partition into (equi-depth) bins: - Bin 1: 4, 8, 9, 15 - Bin 2: 21, 21, 24, 25 - Bin 3: 26, 28, 29, 34 * Smoothing by bin means: - Bin 1: 9, 9, 9, 9 - Bin 2: 23, 23, 23, 23 - Bin 3: 29, 29, 29, 29 * Smoothing by bin boundaries: - Bin 1: 4, 4, 4, 15 - Bin 2: 21, 21, 25, 25 - Bin 3: 26, 26, 26, 34 April 16, 2017 Data Mining: Concepts and Techniques

Simple Discretization Methods: Binning Equal-width (distance) partitioning: It divides the range into N intervals of equal size: uniform grid if A and B are the lowest and highest values of the attribute, the width of intervals will be: W = (B-A)/N. The most straightforward But outliers may dominate presentation Skewed data is not handled well. Equal-depth (frequency) partitioning: It divides the range into N intervals, each containing approximately same number of samples Good data scaling Managing categorical attributes can be tricky. April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Cluster Analysis April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Regression y Y1 Y1’ y = x + 1 X1 x April 16, 2017 Data Mining: Concepts and Techniques

Chapter 3: Data Preprocessing Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Data Integration Data integration: combines data from multiple sources into a coherent store Schema integration integrate metadata from different sources Entity identification problem: identify real world entities from multiple data sources, e.g., A.cust-id  B.cust-# 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 April 16, 2017 Data Mining: Concepts and Techniques

Handling Redundant Data in Data Integration Redundant data occur often when integration of multiple databases The same attribute may have different names in different databases One attribute may be a “derived” attribute in another table, e.g., annual revenue Redundant data may be able to be detected by correlational analysis Careful integration of the data from multiple sources may help reduce/avoid redundancies and inconsistencies and improve mining speed and quality April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Data Transformation Smoothing: remove noise from data Aggregation: summarization, data cube construction Generalization: concept hierarchy climbing Normalization: scaled to fall within a small, specified range min-max normalization z-score normalization normalization by decimal scaling Attribute/feature construction New attributes constructed from the given ones April 16, 2017 Data Mining: Concepts and Techniques

Data Transformation: Normalization min-max normalization z-score normalization normalization by decimal scaling Where j is the smallest integer such that Max(| |)<1 April 16, 2017 Data Mining: Concepts and Techniques

Chapter 3: Data Preprocessing Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Aggregation Combining two or more attributes (or objects) into a single attribute (or object) Purpose Data reduction Reduce the number of attributes or objects Change of scale Cities aggregated into regions, states, countries, etc More “stable” data Aggregated data tends to have less variability April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Aggregation Variation of Precipitation in Australia Standard Deviation of Average Monthly Precipitation Standard Deviation of Average Yearly Precipitation April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Sampling Sampling is the main technique employed for data selection. It is often used for both the preliminary investigation of the data and the final data analysis. Statisticians sample because obtaining the entire set of data of interest is too expensive or time consuming. Sampling is used in data mining because processing the entire set of data of interest is too expensive or time consuming. April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Sampling … The key principle for effective sampling is the following: using a sample will work almost as well as using the entire data sets, if the sample is representative A sample is representative if it has approximately the same property (of interest) as the original set of data April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Types of Sampling Simple Random Sampling There is an equal probability of selecting any particular item Sampling without replacement As each item is selected, it is removed from the population Sampling with replacement Objects are not removed from the population as they are selected for the sample. In sampling with replacement, the same object can be picked up more than once Stratified sampling Split the data into several partitions; then draw random samples from each partition April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Sample Size 8000 points 2000 Points 500 Points April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Sampling Raw Data Cluster/Stratified Sample April 16, 2017 Data Mining: Concepts and Techniques

Curse of Dimensionality When dimensionality increases, data becomes increasingly sparse in the space that it occupies Definitions of density and distance between points, which is critical for clustering and outlier detection, become less meaningful Randomly generate 500 points Compute difference between max and min distance between any pair of points April 16, 2017 Data Mining: Concepts and Techniques

Dimensionality Reduction Purpose: Avoid curse of dimensionality Reduce amount of time and memory required by data mining algorithms Allow data to be more easily visualized May help to eliminate irrelevant features or reduce noise Techniques Principle Component Analysis Singular Value Decomposition Others: supervised and non-linear techniques April 16, 2017 Data Mining: Concepts and Techniques

Feature Subset Selection Another way to reduce dimensionality of data Redundant features duplicate much or all of the information contained in one or more other attributes Example: purchase price of a product and the amount of sales tax paid Irrelevant features contain no information that is useful for the data mining task at hand Example: students' ID is often irrelevant to the task of predicting students' GPA April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Feature Creation Create new attributes that can capture the important information in a data set much more efficiently than the original attributes Three general methodologies: Feature Extraction domain-specific Mapping Data to New Space Feature Construction combining features April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Clustering Partition data set into clusters, and one can store cluster representation only Can be very effective if data is clustered but not if data is “smeared” Can have hierarchical clustering and be stored in multi-dimensional index tree structures There are many choices of clustering definitions and clustering algorithms, further detailed in Chapter 8 April 16, 2017 Data Mining: Concepts and Techniques

Chapter 3: Data Preprocessing Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary April 16, 2017 Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques Discretization Three types of attributes: Nominal — values from an unordered set Ordinal — values from an ordered set Continuous — real numbers Discretization: divide the range of a continuous attribute into intervals Some classification algorithms only accept categorical attributes. Reduce data size by discretization Prepare for further analysis April 16, 2017 Data Mining: Concepts and Techniques

Discretization and Concept hierachy reduce the number of values for a given continuous attribute by dividing the range of the attribute into intervals. Interval labels can then be used to replace actual data values. Concept hierarchies reduce the data by collecting and replacing low level concepts (such as numeric values for the attribute age) by higher level concepts (such as young, middle-aged, or senior). April 16, 2017 Data Mining: Concepts and Techniques

Concept hierarchy generation for categorical data Specification of a partial ordering of attributes explicitly at the schema level by users or experts Specification of a portion of a hierarchy by explicit data grouping Specification of a set of attributes, but not of their partial ordering Specification of only a partial set of attributes April 16, 2017 Data Mining: Concepts and Techniques

Specification of a set of attributes Concept hierarchy can be automatically generated based on the number of distinct values per attribute in the given attribute set. The attribute with the most distinct values is placed at the lowest level of the hierarchy. 15 distinct values country province_or_ state 65 distinct values 3567 distinct values city 674,339 distinct values April 16, 2017 street Data Mining: Concepts and Techniques