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Chapter 7 . DATA, TEXT, AND WEB MINING. Learning Objectives. Define data mining and list its objectives and benefits Understand different purposes and applications of data mining Understand different methods of data mining, especially clustering and decision tree models

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chapter 7

Chapter 7

DATA, TEXT,

AND WEB MINING

learning objectives
Learning Objectives
  • Define data mining and list its objectives and benefits
  • Understand different purposes and applications of data mining
  • Understand different methods of data mining, especially clustering and decision tree models
  • Build expertise in use of some data mining software
learning objectives1
Learning Objectives
  • Learn the process of data mining projects
  • Understand data mining pitfalls and myths
  • Define text mining and its objectives and benefits
  • Appreciate use of text mining in business applications
  • Define Web mining and its objectives and benefits
data mining concepts and applications
Data Mining Concepts and Applications
  • Six factors behind the sudden rise in popularity of data mining
    • General recognition of the untapped value in large databases;
    • Consolidation of database records tending toward a single customer view;
    • Consolidation of databases, including the concept of an information warehouse;
    • Reduction in the cost of data storage and processing, providing for the ability to collect and accumulate data;
    • Intense competition for a customer’s attention in an increasingly saturated marketplace; and
    • The movement toward the de-massification of business practices
data mining concepts and applications1
Data Mining Concepts and Applications
  • Data mining (DM)

A process that uses statistical, mathematical, artificial intelligence and machine-learning techniques to extract and identify useful information and subsequent knowledge from large databases

data mining concepts and applications2
Data Mining Concepts and Applications
  • Major characteristics and objectives of data mining
    • Data are often buried deep within very large databases, which sometimes contain data from several years; sometimes the data are cleansed and consolidated in a data warehouse
    • The data mining environment is usually client/server architecture or a Web-based architecture
data mining concepts and applications3
Data Mining Concepts and Applications
  • Major characteristics and objectives of data mining
    • Sophisticated new tools help to remove the information ore buried in corporate files or archival public records; finding it involves massaging and synchronizing the data to get the right results.
    • The miner is often an end user, empowered by data drills and other power query tools to ask ad hoc questions and obtain answers quickly, with little or no programming skill
data mining concepts and applications4
Data Mining Concepts and Applications
  • Major characteristics and objectives of data mining
    • Striking it rich often involves finding an unexpected result and requires end users to think creatively
    • Data mining tools are readily combined with spreadsheets and other software development tools; the mined data can be analyzed and processed quickly and easily
    • Parallel processing is sometimes used because of the large amounts of data and massive search efforts
data mining concepts and applications5
Data Mining Concepts and Applications
  • How data mining works
    • Data mining tools find patterns in data and may even infer rules from them
    • Three methods are used to identify patterns in data:
      • Simple models
      • Intermediate models
      • Complex models
data mining concepts and applications6
Data Mining Concepts and Applications
  • Classification

Supervised induction used to analyze the historical data stored in a database and to automatically generate a model that can predict future behavior

  • Common tools used for classification are:
    • Neural networks
    • Decision trees
    • If-then-else rules
data mining concepts and applications7
Data Mining Concepts and Applications
  • Clustering

Partitioning a database into segments in which the members of a segment share similar qualities

  • Association

A category of data mining algorithm that establishes relationships about items that occur together in a given record

data mining concepts and applications8
Data Mining Concepts and Applications
  • Sequence discovery

The identification of associations over time

  • Visualization can be used in conjunction with data mining to gain a clearer understanding of many underlying relationships
data mining concepts and applications9
Data Mining Concepts and Applications
  • Regression is a well-known statistical technique that is used to map data to a prediction value
  • Forecasting estimates future values based on patterns within large sets of data
data mining concepts and applications10
Data Mining Concepts and Applications
  • Hypothesis-driven data mining

Begins with a proposition by the user, who then seeks to validate the truthfulness of the proposition

  • Discovery-driven data mining

Finds patterns, associations, and relationships among the data in order to uncover facts that were previously unknown or not even contemplated by an organization

data mining concepts and applications11
Marketing

Banking

Retailing and sales

Manufacturing and production

Brokerage and securities trading

Insurance

Computer hardware and software

Government and defense

Airlines

Health care

Broadcasting

Police

Homeland security

Data Mining Concepts and Applications

Data mining applications

data mining techniques and tools
Data Mining Techniques and Tools
  • Data mining tools and techniques can be classified based on the structure of the data and the algorithms used:
    • Statistical methods
    • Decision trees

Defined as a root followed by internal nodes. Each node (including root) is labeled with a question and arcs associated with each node cover all possible responses

data mining techniques and tools1
Data Mining Techniques and Tools
  • Data mining tools and techniques can be classified based on the structure of the data and the algorithms used:
    • Case-based reasoning
    • Neural computing
    • Intelligent agents
    • Genetic algorithms
    • Other tools
      • Rule induction
      • Data visualization
data mining techniques and tools2
Data Mining Techniques and Tools
  • A general algorithm for building a decision tree:
    • Create a root node and select a splitting attribute.
    • Add a branch to the root node for each split candidate value and label
    • Take the following iterative steps:
      • Classify data by applying the split value.
      • If a stopping point is reached, then create leaf node and label it. Otherwise, build another subtree
data mining techniques and tools3
Data Mining Techniques and Tools
  • Gini index

Used in economics to measure the diversity of the population. The same concept can be used to determine the ‘purity’ of a specific class as a result of a decision to branch along a particular attribute/variable

data mining techniques and tools4
Data Mining Techniques and Tools
  • The ID3 algorithm decision tree approach
    • Entropy

Measures the extent of uncertainty or randomness in a data set. If all the data in a subset belong to just one class, then there is no uncertainty or randomness in that dataset, therefore the entropy is zero

data mining techniques and tools5
Data Mining Techniques and Tools
  • Cluster analysis for data mining
    • Cluster analysis is an exploratory data analysis tool for solving classification problems
    • The object is to sort cases into groups so that the degree of association is strong between members of the same cluster and weak between members of different clusters
data mining techniques and tools6
Data Mining Techniques and Tools
  • Cluster analysis results may be used to:
    • Help identify a classification scheme
    • Suggest statistical models to describe populations
    • Indicate rules for assigning new cases to classes for identification, targeting, and diagnostic purposes
    • Provide measures of definition, size, and change in what were previously broad concepts
    • Find typical cases to represent classes
data mining techniques and tools7
Data Mining Techniques and Tools
  • Cluster analysis methods
    • Statistical methods
    • Optimal methods
    • Neural networks
    • Fuzzy logic
    • Genetic algorithms
  • Each of these methods generally works with one of two general method classes:
    • Divisive
    • Agglomerative
data mining techniques and tools8
Data Mining Techniques and Tools
  • Hierarchical clustering method and example
    • Decide which data to record from the items
    • Calculate the distances between all initial clusters. Store the results in a distance matrix
    • Search through the distance matrix and find the two most similar clusters
    • Fuse those two clusters together to produce a cluster that has at least two items
    • Calculate the distances between this new cluster and all the other clusters
    • Repeat steps 3 to 5 until you have reached the prespecified maximum number of clusters
data mining techniques and tools9
Data Mining Techniques and Tools
  • Classes of data mining tools and techniques as they relate to information and business intelligence (BI) technologies
    • Mathematical and statistical analysis packages
    • Personalization tools for Web-based marketing
    • Analytics built into marketing platforms
    • Advanced CRM tools
    • Analytics added to other vertical industry-specific platforms
    • Analytics added to database tools (e.g., OLAP)
    • Standalone data mining tools
data mining project processes2
Data Mining Project Processes
  • Knowledge discovery in databases (KDD)

A comprehensive process of using data mining methods to find useful information and patterns in data

data mining project processes3
Data Mining Project Processes
  • KDD process
    • Selection
    • Preprocessing
    • Transformation
    • Data mining
    • Interpretation/evaluation
text mining
Text Mining
  • Text mining

Application of data mining to nonstructured or less structured text files. It entails the generation of meaningful numerical indices from the unstructured text and then processing these indices using various data mining algorithms

text mining1
Text Mining
  • Text mining helps organizations:
    • Find the “hidden” content of documents, including additional useful relationships
    • Relate documents across previous unnoticed divisions
    • Group documents by common themes
text mining2
Text Mining
  • Applications of text mining
    • Automatic detection of e-mail spam or phishing through analysis of the document content
    • Automatic processing of messages or e-mails to route a message to the most appropriate party to process that message
    • Analysis of warranty claims, help desk calls/reports, and so on to identify the most common problems and relevant responses
text mining3
Text Mining
  • Applications of text mining
    • Analysis of related scientific publications in journals to create an automated summary view of a particular discipline
    • Creation of a “relationship view” of a document collection
    • Qualitative analysis of documents to detect deception
text mining4
Text Mining
  • How to mine text
    • Eliminate commonly used words (stop-words)
    • Replace words with their stems or roots (stemming algorithms)
    • Consider synonyms and phrases
    • Calculate the weights of the remaining terms
web mining
Web Mining
  • Web mining

The discovery and analysis of interesting and useful information from the Web, about the Web, and usually through Web-based tools

web mining1
Web Mining
  • Web content mining

The extraction of useful information from Web pages

  • Web structure mining

The development of useful information from the links included in the Web documents

  • Web usage mining

The extraction of useful information from the data being generated through webpage visits, transaction, etc.

web mining2
Web Mining
  • Uses for Web mining:
    • Determine the lifetime value of clients
    • Design cross-marketing strategies across products
    • Evaluate promotional campaigns
    • Target electronic ads and coupons at user groups
    • Predict user behavior
    • Present dynamic information to users