DATA MINING KNOWLEDGE DISCOVERY

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DATA MINING(KNOWLEDGE DISCOVERY)

• Data mining is an attempt at knowledge discovery
  searching for patterns and structure in large
  data sets.
• OLAP is about confirming the known, we might say
  that data mining is about exploring the unknown.
• Data mining uses a blend of statistical analysis,
  AI, and computer graphics techniques

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GOALS OF DATA MINING

• Explanatory To explain some observed event or
  condition, such as why sales of pickup trucks
  have increased in Colorado
• Confirmatory To confirm a hypothesis, such as
  whether two-income families are more likely to
  buy family medical coverage than single-income
  families
• Exploratory To analyze data for new or
  unexpected relationships, such as what spending
  patterns are likely to accompany credit card fraud

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• TECHNIQUES USED IN DATA MINING
• Association rules - e.g., whenever a customer
  buys video equipment, he or she also buys another
  electronic gadget.
• Sequential patterns e.g., suppose a customer
  buys a camera, and within three months he or she
  buys photographic supplies, and within six months
  an accessory item. A customer who buys more than
  twice in the lean periods may be likely to buy at
  least once during Christmas period.
• Classification trees e.g., customers may be
  classified by frequency of visits, by types of
  financing used, by amount of purchase.

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Extraction Of Knowledge From Data
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Four Phases of Data Mining

• 1. Data Preparation
• Identify and cleanse data sets.
• Data Warehouse is usually used for data mining
  operations.
• E.g a database consumer goods retailer.
• Client data cust_name, zip code, phone number,
  date of purchase, item code, price, qty, total
  amt.

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• 2. Data Analysis and Classification
• Identify common data characteristics or patterns
  using
• Data groupings, classifications, clusters, or
  sequences.
• Data dependencies, links, or relationships.
• Data patterns, trends, and deviations.
• E.g categories of items, from stores in a
  specific region

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• 3. Knowledge Acquisition
• Select the appropriate modeling or knowledge
  acquisition algorithms.
• Examples neural networks, decision trees, rules
  induction, genetic algorithms, classification and
  regression tree, memory-based reasoning, or
  nearest neighbor and data visualization.
• e.g People who are in the salary range gtRM5
  per month are likely to spend RM1K for groceries.

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• 4. Prognosis
• Predict future behavior and forecast business
  outcomes using the data mining findings.
• E.g
• 65 of customers who did not use a particular
  credit card in the last six months are 88 likely
  to cancel that account.
• 82 of customers who bought a new TV 27 inches or
  larger are 90 likely to buy an entertainment
  center within the next four weeks

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Data-Mining Phases