Week 9

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Week 9

• Data Mining System
• (Knowledge Data Discovery)

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Case Scenario

• ABC Enterprise is a multinational company that
  offers multimedia content services in several
  regions in Asia. It has more than 6 millions
  content subscribers. For a company of this size,
  another major problem is to maintain good
  relationship with their existing content
  subscribers. Every year, they have to offer good
  content promotion to suit their customer needs.
  However, this is a difficult task because they
  have huge collection of data about their
  subscribers which have different needs and
  lifestyle. Therefore, the CEO of the company, Mr.
  Ridzuan wishes that there is a system that can be
  built to analyze enormous data about their
  subscribers and can suggest what kind of content
  promotions suitable for them.

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Knowledge Discovery Data Mining

• Knowledge Discovery (KD) is a process of
  extracting previously unknown, valid, and
  actionable (understandable) information from
  large databases.
• Data mining is a step in the KDD process of
  applying data analysis and discovery algorithms.
• Relates to machine learning, pattern recognition,
  statistics, data visualization etc.

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• Knowledge discovery in databases (KDD) is the
  non-trivial process of identifying valid,
  potentially useful and ultimately understandable
  patterns in data.

Data Mining
Clean, Collect, Summarize
Data Preparation
Training Data
Data Warehouse
Model Patterns
Verification, Evaluation
Operational Databases
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Why Mine Data?

• Huge amounts of data being collected and
  warehoused
• Walmart records 20 millions per day
• health care transactions multi-gigabyte
  databases
• Mobil Oil geological data of over 100 terabytes
• Affordable computing
• Competitive pressure
• gain an edge by providing improved, customized
  services
• information as a product in its own right

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Data Mining Methods

• Prediction Methods
• using some variables to predict unknown or future
  values of other variables
• Descriptive Methods
• finding human-interpretable patterns describing
  the data

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Data Mining Tasks

• Classification
• Clustering
• Association Rule Discovery
• Sequential Pattern Discovery

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1. Classification

• Data defined in terms of attributes, one of which
  is the class.
• Find a model for class attribute as a function of
  the values of other(predictor) attributes, such
  that previously unseen records can be assigned a
  class as accurately as possible.

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ClassificationExample
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Classification Direct Marketing

• Goal Reduce cost of soliciting (mailing) by
  targeting a set of consumers likely to buy a new
  product.
• Data
• for similar product introduced earlier
• we know which customers decided to buy and which
  did not buy, not buy class attribute
• collect various demographic, lifestyle, and
  company related information about all such
  customers - as possible predictor variables.
• Learn classifier model

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2. Clustering

• Given a set of data points, each having a set of
  attributes, and a similarity measure among them,
  find clusters such that
• data points in one cluster are more similar to
  one another
• data points in separate clusters are less similar
  to one another.
• Similarity measures
• Euclidean distance if attributes are continuous
• Problem specific measures

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Clustering Market Segmentation

• Goal subdivide a market into distinct subsets of
  customers where any subset may conceivably be
  selected as a market target to be reached with a
  distinct marketing mix.
• Approach
• collect different attributes on customers based
  on geographical, and lifestyle related
  information
• identify clusters of similar customers
• measure the clustering quality by observing
  buying patterns of customers in same cluster vs.
  those from different clusters.

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3. Association Rule Discovery

• Given a set of records, each of which contain
  some number of items from a given collection
• produce dependency rules which will predict
  occurrence of an item based on occurences of
  other items

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Association Rule Discovery
Marketing and Sales Promotion Application
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4. Sequential Pattern Discovery

• Given set of objects, each associated with its
  own timeline of events, find rules that predict
  strong sequential dependencies among different
  events, of the form (A B) (C) (D E) --gt (F)

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Sequential Pattern Discovery Examples

• sequences in which customers purchase
  goods/services
• understanding long term customer behavior --
  timely promotions.
• In point-of--sale transaction sequences
• Athletic Apparel Store
• (Shoes) (Racket, Racketball) --gt (Sports
  Jacket)

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Data Mining Systems

• Clementine (SPSS)
• http//www.spss.com/spssbi/clementine/index.htm
• Data Miner (Statistica)
• http//www.statsoft.com/dataminer.html
• RuleQuest (C5.0)
• http//www.rulequest.com/

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Limitation/Challenges

• large data
• number of variables (features), number of cases
  (examples)
• multi gigabyte, terabyte databases
• efficient algorithms, parallel processing
• high dimensionality
• large number of features exponential increase in
  search space (potential for spurious patterns)
• Use of domain knowledge
• utilizing knowledge on complex data
  relationships, known facts

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Intelligence Density Dimension

• Accuracy
• Explainability
• Flexibility
• Response speed