Data Mining Introduction PDF

Chapter 1. Introduction ❑ What Is Data Mining? ❑ Data Mining: An Essential Step in Knowledge Discovery ❑ Diversity of Data Types for Data Mining ❑ Mining Various Kinds of Knowledge ❑ Data Mining: Confluence of Multiple Disciplines ❑ Data Mining and Applications...

Chapter 1. Introduction ❑ What Is Data Mining? ❑ Data Mining: An Essential Step in Knowledge Discovery ❑ Diversity of Data Types for Data Mining ❑ Mining Various Kinds of Knowledge ❑ Data Mining: Confluence of Multiple Disciplines ❑ Data Mining and Applications ❑ Data Mining and Society ❑ Summary 1  What Is Data Mining? ❑ Data mining (knowledge discovery from data) ❑ Data mining is the process of discovering interesting patterns, models and other kinds of knowledge in large data sets ❑ (non-trivial, implicit, previously unknown and potentially useful) patterns ❑ Alternative names ❑ Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. 2 2  Data Mining: An Essential Step in Knowledge Discovery ❑ Knowledge Discovery Process ❑ Data preparation ❑ Data cleaning ❑ Data integration ❑ Data transformation ❑ Data selection ❑ Data mining ❑ Pattern/model evaluation ❑ Knowledge presentation 3  Diversity of Data Types for Data Mining (I) ❑ Structured vs. unstructured data ❑ Structured: uniform, record- or table-like structures, defined by data dictionaries, with a fixed set of attributes, each with a fixed set of value ranges and semantic meaning ❑ Ex. Data stored in relational databases, data cubes, data matrices, and many data warehouses ❑ Semi-structured: allow a data object to contain a set value, a small set of heterogeneous typed values, or nested structures, or to allow the structure of objects or sub-objects to be defined flexibly and dynamically ❑ Data having certain structures with clearly defined semantic meaning, such as transactional data set, sequence data set (e.g., time-series data, gene or protein data, or Weblog data) ❑ Graph or network data: A more sophisticated type of semi-structured data set ❑ Unstructured data: text data and multimedia (e.g., audio, image, video) data ❑ The real-world data can often be a mixture of structured, semi-structured data and 4 unstructured data  Diversity of Data Types for Data Mining (II) ❑ Data associated with different applications Different applications: different data sets and require different data analysis methods ❑ ❑ Sequence data: Biological sequences vs. shopping transaction sequences ❑ Time-series: ordered set of numerical values with equal time interval ❑ Spatial, temporal and spatiotemporal data ❑ Graph and network data: Social networks, computer communication networks, biological networks, and information networks may carry rather different semantics ❑ On the same data set, finding different kinds of patterns: require different mining methods ❑ Ex. software programs: finding plagiarized modules vs. finding copy-and-paste bugs ❑ Stored vs. streaming data ❑ Stored data: Finite, stored in various kinds of large data repositories ❑ Streaming data (e.g., video surveillance or remote sensing): Dynamic, constantly coming, infinite, real-time response―posing challenges on effective data mining 5  Mining Various Kinds of Knowledge ❑ Multidimensional Data Summarization ❑ Mining Frequent Patterns, Associations, and Correlations ❑ Classification and Regression for Predictive Analysis ❑ Cluster Analysis ❑ Deep Learning ❑ Outlier Analysis ❑ Are All Mining Results Interesting? 6  Multidimensional Data Summarization ❑ Information integration and data warehouse construction ❑ Data cleaning, transformation, integration, and multidimensional data model ❑ Data cube technology ❑ Scalable methods for computing (i.e., materializing) multidimensional aggregates ❑ OLAP (online analytical processing) ❑ Multidimensional concept description: Characterization and discrimination ❑ Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet region 7  Pattern Discovery: Mining Frequent Patterns, Associations, and Correlations ❑ Frequent patterns (or frequent itemsets) ❑ What items are frequently purchased together in your Walmart? ❑ Association and Correlation Analysis ❑ A typical association rule ❑ Diaper → Beer [0.5%, 75%] (support, confidence) ❑ Are strongly associated items also strongly correlated? ❑ How to mine such patterns and rules efficiently in large datasets? ❑ How to use such patterns for classification, clustering, and other applications? 8  Classification and Regression for Predictive Analysis ❑ Classification and label prediction ❑ Construct models (functions) based on some training examples ❑ Describe and distinguish classes or concepts for future prediction ❑ Ex. 1. Classify countries based on (climate) ❑ Ex. 2. Classify cars based on (gas mileage) ❑ Predict some unknown class labels ❑ Typical methods ❑ Decision trees, naïve Bayesian classification, support vector machines, neural networks, rule-based classification, pattern-based classification, logistic regression, … ❑ Typical applications: ❑ Credit card fraud detection, direct marketing, classifying stars, diseases, web- pages, … 9  Cluster Analysis ❑ Unsupervised learning (i.e., Class label is unknown) ❑ Group data to form new categories (i.e., clusters), e.g., cluster houses to find distribution patterns ❑ Principle: Maximizing intra-class similarity & minimizing interclass similarity ❑ Many methods and applications 10  Deep Learning ❑ Deep learning: A fast expanding dynamic frontier in machine learning ❑ Deep learning has developed various neural network architectures ❑ Feed-forward neural networks ❑ Convolutional neural networks ❑ Recurrent neural networks ❑ Graph neural networks ❑ Transformer ❑ Deep learning has broad applications in computer vision, natural language processing, machine translation, social network analysis, and so on ❑ Deep learning has been reshaping a variety of data mining tasks ❑ Ex. classification, clustering, outlier detection, and reinforcement learning 11  Outlier Analysis ❑ Outlier analysis ❑ Outlier: A data object that does not comply with the general behavior of the data ❑ Noise or exception?―One person’s garbage could be another person’s treasure ❑ Methods: by product of clustering or regression analysis, … ❑ Useful in fraud detection, rare events analysis 12  Other Data Mining Functions: Time and Ordering: Sequential Pattern, Trend and Evolution Analysis ❑ Sequence, trend and evolution analysis ❑ Trend, time-series, and deviation analysis ❑ e.g., regression and value prediction ❑ Sequential pattern mining ❑ e.g., buy digital camera, then buy large memory cards ❑ Periodicity analysis ❑ Motifs and biological sequence analysis ❑ Approximate and consecutive motifs ❑ Similarity-based analysis ❑ Mining data streams ❑ Ordered, time-varying, potentially infinite, data streams 13  Other Data Mining Functions: Structure and Network Analysis ❑ Graph mining ❑ Finding frequent subgraphs (e.g., chemical compounds), trees (XML), substructures (web fragments) ❑ Information network analysis ❑ Social networks: actors (objects, nodes) and relationships (edges) ❑ e.g., author networks in CS, terrorist networks ❑ Multiple heterogeneous networks ❑ A person could be multiple information networks: friends, family, classmates, … ❑ Links carry a lot of semantic information: Link mining ❑ Web mining ❑ Web is a big information network: from PageRank to Google ❑ Analysis of Web information networks ❑ Web community discovery, opinion mining, usage mining, … 14  Evaluation of Knowledge ❑ Evaluation of mined knowledge → directly mining only interesting knowledge? ❑ Descriptive vs. predictive ❑ Coverage ❑ Typicality vs. novelty ❑ Accuracy ❑ Timeliness ❑ … 15  Data Mining: Confluence of Multiple Disciplines 16  Data Mining and Applications ❑ Web page analysis: classification, clustering, ranking ❑ Collaborative analysis & recommender systems ❑ Basket data analysis to targeted marketing ❑ Biological and medical data analysis ❑ Data mining and software engineering ❑ Data mining and text analysis ❑ Data mining and social and information network analysis ❑ Built-in (invisible data mining) functions in Google, Microsoft, LinkedIn, Meta, … ❑ Major dedicated data mining systems/tools ❑ SAS, MS SQL-Server Analysis Manager, Oracle Data Mining Tools) 17