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  • 1. ICT619 Intelligent Systems Topic 6: Data Mining
  • 2. Data Mining
    • Introduction
    • Business Applications of Data Mining
    • Data Mining Activities
    • Data Mining Techniques
    • How to Apply Data Mining
    • Data Mining Development Methodology
  • 3. Why data mining?
    • “ Customers who bought this title also bought … “
    • - from Amazon.com
      • Why? – More effective (targeted) marketing
      • How? – Targeting through association
    • Abundance of business data typically in terabytes
    • - point-of-sale (POS) devices, customer call detail databases, web log files in e-commerce etc
    • Data is being collected mostly for improving efficiency of underlying operations
    • But not for analysis
  • 4. Why data mining? (cont’d)
    • Useful information (business intelligence) to gain competitive advantage can be extracted by "mine"-ing data
    • Examples: underlying trends, associations or patterns in market behaviour
    • According to (Hirji 2001),
    • “ … data mining is the analysis and non-trivial extraction of data from databases for the purpose of discovering new and valuable information, in the form of patterns and rules, from relationships between data elements.”
  • 5. Data mining in perspective
    • OLAP with data warehouses tells us what is happening and how
    • Data mining tells us what is likely to happen
    • Data mining is knowledge discovery in (commercial) databases - KDD
    • Data mining is a process rather than a product
  • 6. Data mining in perspective (cont’d)
    • Statistical methods do not scale up to today's problems
    • New "intelligent" tools are needed
    • Data mining draws from artificial intelligence/soft computing, database theory, data visualization, marketing, statistics, and so on
    • Our objectives:
      • Understand the role of data mining in business
      • Distinguish between different data mining techniques
      • Understand how to go about making use of data mining
  • 7. Business Applications of Data Mining
    • Fastest growing segment of business intelligence market
    • Increasingly an integral and necessary component of an organization’s portfolio of analytical techniques
    • Data mining for marketing
    • Uses data on customer behaviour to identify target groups for marketing
    • Reduces cost by avoiding groups unlikely to respond
  • 8. Business Applications of Data Mining (cont’d)
    • Data mining for customer relationship management
      • Anticipating customers’ needs and responding to them proactively
    • Data mining in R&D
      • Can lower costs during the R&D phase of the product life cycle by analysing voluminous test data
      • Bioinformatics - data mining in biology and medicine
  • 9. Data Mining Activities
    • Two broad groups – directed and undirected data mining
    • Directed data mining
    • We know what we are looking for
    • We aim to find the value of a pre-identified target variable in terms of a collection of input variables, eg, classifying insurance claims
    • Undirected data mining
    • Finds patterns in data
    • Leaves it to the user to find the significance of these patterns
    • Eg, identifying groups of customers with similar buying patterns
  • 10. Different types of data mining tasks
  • 11. Data Mining Tasks
    • Classification
    • Assigns a given object to a predefined category (class) based on the object’s attributes (features)
    • Objects to be classified are generally database records.
    • Discrete outcomes – yes/no, low/medium/high etc,
    • Examples of classification tasks:
      • Assigning keywords to articles
      • Classifying credit applicants as low, medium and high risk
      • Assigning customers to predefined customer segments
  • 12. Data Mining Tasks
    • Estimation
    • Continuously varying outcomes
    • Eg income, probability of a customer leaving (known in data mining circles as churning )
    • Outcomes can also be used for classification by ranking and thresholding
    • Prediction
    • Classification or estimation task performed to predict some future behaviour
    • Examples include:
    • - Predicting which customers will churn in the next six months
    • - Predicting the size of a balance that will be transferred
  • 13. Data Mining Tasks (cont’d)
    • Finding affinity grouping or association rules
    • Finds out, which things go together, eg, in a supermarket shopping trolley
    • Used for arranging items in shelves or catalogues
    • Identifying cross-selling opportunities
    • Clustering
    • Segments a group of diverse records into subgroups or clusters containing similar records
    • No predefined classes in clustering; records grouped based on similarities in their attributes
    • Eg, people with similar buying habits
    • Data miner must interpret clusters and decide what to do
  • 14. Data Mining Tasks (cont’d)
    • Description and visualisation
    • To help increase our understanding of people, products or processes that produced the data
    • A good description can provide an explanation of their behaviour
    • Data visualisation can be very effective in explaining things by exploiting our ability to utilise visual clues
  • 15. Data Mining Techniques
    • Our aim is a basic understanding of data mining techniques to find out
      • When to apply them
      • How to interpret their results
      • How to evaluate their performance
    • Three major approaches are:
      • Decision trees
      • Automatic cluster detection
      • Artificial neural networks (supervised and unsupervised)
  • 16. Decision Trees
    • Visual representation of a reasoning process
    • Particularly suitable for solving classification problems
    • Consists of internal nodes, leaf nodes and edges
    Fig. A sample decision tree for catalogue mailing (Ganti et al. 1999).
  • 17. Decision Trees (cont’d)
    • Each leaf node is labelled with a class label
    • The class label decided by the class of the records that ended up in that leaf during training
    • A leaf node may also contain a value depending upon the average of the values of such records
    Fig. A sample decision tree for catalogue mailing (Ganti et al. 1999). Group A contains any self-employed person aged <=40 and earning a salary of more than $50,000.
  • 18. Decision Trees (cont’d)
    • Each edge originating from an internal node is labelled with a splitting predicate involving that node’s splitting attribute
    • The splitting predicate forces any record to take a unique path from the root to exactly one leaf node.
    Fig. A sample decision tree for catalogue mailing (Ganti et al. 1999). Group A contains any self-employed person aged less than 41 and earning a salary of more than $50,000.
  • 19. How decision trees work
    • Each record with N attributes is a point in an N -dimensional record space
    • Each branch in a decision tree is a test on a single variable that splits the space into two or more regions
    • With each successive test and split, the resulting regions get more and more segregated with increasing homogeneity among the records
    • Ultimately, the leaf nodes will contain the purest batch of records
  • 20. How decision trees work
    • For example, in the example decision tree, any self-employed person aged <= 40 and earning a salary of more than $50,000 will be classified as belonging to group A.
  • 21. How decision trees work (cont’d)
    • Overfitting in decision trees
    • A decision tree that correctly classifies every single record
    • Such a tree is unlikely to generalise to new data sets
    • To prevent overfitting, test data set are used to prune decision trees once it has been built using the training data set.
  • 22. How decision trees are built
    • Recursive partitioning
    • An iterative process of splitting the training data into partitions (regions of record space)
    • Initially, all records are in a training set consisting of pre-classified records
    • An algorithm splits up the data, using every possible binary split on every field of the records
    • The best split is defined as one that creates partitions where a single class predominates
  • 23. How decision trees are built (cont’d)
    • Recursive partitioning (cont’d)
    • The most important task in building a decision tree is to decide which of the attributes (independent fields in a record) gives the best split
    • The measure used to evaluate a potential splitter is the reduction in diversity (or increase in purity)
    • The best split has the largest reduction in diversity
    • One measure of diversity is the Gini index:
    • 2p1 * (1 – p2)
  • 24. How decision trees are built (cont’d)
    • Recursive partitioning (cont’d)
    • The splitting process is applied to each of the new parts and so on until no more useful splits can be found
    • A node becomes a leaf node when no split can be found that significantly decreases the diversity
    • Pruning
    • The full decision tree needs to be pruned to improve its performance
    • Pruning is done by removing leaves and branches (edges leading to leaves) that fail to generalise
    • There are a number of pruning methods
    • Eg, a tree is pruned back to the subtree that minimises error on the test set.
  • 25. How decision trees are built (cont’d)
    • Different types of decision trees
    • Types depend upon
      • the number of splits allowed at each level
      • how these splits are chosen when the tree is built
      • how the tree is pruned to prevent overfitting
    • More broadly, decision trees can be grouped as:
      • Classification trees (leaves represent classes)
      • Regression trees (leaves represent a numeric value)
  • 26. Algorithms for building decision trees
    • Most notable are
    • - CHAID, C4.5/C5.0 and CART
    • Data mining software tools allow approximation of any of these algorithms by providing choice of
      • splitting criteria and pruning strategies
      • control parameters such as maximum tree depth
  • 27. Application of decision trees
    • Useful when the data mining task is classification of records or prediction of outcomes
    • Also chosen to generate understandable rules, which can be explained and translated into SQL or a natural language
    • For example,
      • IF age < 41
      • AND income < $50,000
      • AND employment = self
      • THEN belongs to group A
  • 28. Automatic Cluster Detection
    • Aims to discover structure in a complex data set as a whole in order to carve it up into simpler groups
    • Examples of clustering
    • - finding products that should be grouped together in a catalogue, or
    • - identifying groups of customers with similar tastes in music
    • Many methods for finding clusters in data, a prominent one is K-means clustering
  • 29. K-means clustering
    • Available in a wide variety of commercial data mining tools
    • Divides the data set into a predetermined number, k, of clusters
    • Initial clusters centred at random points ( seeds ) in the record space
  • 30. K-means clustering (cont’d)
    • Records are assigned to the clusters through an iterative process
    • In the first step, k data points are selected to be the seeds
    • Each seed is an embryonic cluster with only one element
    • In the second step, each record is assigned to the cluster whose centroid is nearest to that record
    • This forms the new clusters with new intercluster boundaries.
  • 31. K-means clustering (cont’d)
    • The centroid of a cluster of records calculated by taking average of each field for all the records in that cluster
    • Euclidean distance most commonly used for measuring distance by data mining software.
    • Distance between two points P(x1, x2, .. , xn) and Q(y1, y2, .. , yn) in n -dimensional space is  ((x1-y1) 2 + (x2-y2) 2 + .. + (xn-yn) 2 ).
  • 32. K-means clustering (cont’d)
    • In the k -means method, the original choice of the value of k determines the number of clusters that will be found
    • Unless advanced knowledge is available on the likely number of clusters, value of k is determined by trial-and-error
    • Best results are obtained when k matches the underlying structure of the data.
  • 33. Interpreting clusters
    • Automatic clustering is undirected data mining
    • - We look for something useful without having to know what we are looking for
    • Both an advantage and possible disadvantage!
    • The most frequently used approaches interpreting clusters are
      • Building a decision tree with the cluster labels as target variables, and using it to derive rules explaining how to assign new records to the correct cluster
      • Using visualisation to see how the clusters are affected by changes in input variables.
      • Examining the differences in the distributions of variables from cluster to cluster, one variable at a time.
  • 34. Application of clusters
    • Clustering is used
    • When natural groupings are suspected,
    • Eg, groups representing customers or products that have a lot in common with each other
    • When there are many competing patterns in the data making it hard to spot any single pattern
    • Creating clusters reduces the complexity within clusters so that other data mining techniques are more likely to succeed
  • 35. Artificial Neural Networks
    • Main generic application of artificial neural networks is pattern recognition or classification
    • Estimation and prediction can be viewed as variants of classification
    • The best ANN model for performing classification is the backpropagation network (or the multilayer perceptron)
    • The ANN model particularly suited for clustering is the Kohonen net or the self-organising map (SOM)
  • 36. Artificial Neural Networks (cont’d)
    • SOM learning algorithms are unsupervised
    • Clusters are represented in a SOM by groups of adjacent neurons in output layer
    • SOM reduces dimensionality from N to 2
    • SOM can serve as a clustering tool as well as visualisation tool for high-dimensional data
    • SOMs claimed to be often more effective than k -means for complex shaped clusters
  • 37. Application of neural nets
    • Artificial neural networks can produce very good results
    • But require extensive data preparation involving normalisation and conversion of categorical values to numeric values
    • Do not work well when there are many hundreds or thousands of input features - long training phases
    • Difficult to understand because they represent complex non-linear models
    • Unlike decision trees, do not produce rules readily.
    • A good choice for most classification and prediction tasks when the results are more important than understanding how the model works
  • 38. How to Apply Data Mining
    • Four ways of utilising data mining expertise in business:
    • By purchasing readymade scores (such as on credit worthiness for a loan applicant) from outside vendors.
    • By purchasing software that embodies data mining expertise designed for a particular application such as credit approval, fraud detection or churn prevention
    • By hiring outside consultants to perform data mining for special projects
    • By developing own data mining skills within the business organization
    • Purchasing scores is quick and easy, but the intelligence limited to single score values
  • 39. Purchasing Software
    • Two possibilities:
    • Software may be an actual model
    • Eg, in the form of a set of rules for decision support, or a fully-trained neural network applied to a particular domain
    • Software may embody knowledge of the process of building models for a particular domain in the form of a model-creation wizard or template
    • Purchased models work well if the products, customers, and market conditions match those used to develop the model
  • 40. Tasks for the data mining model builder
    • Model building software automate the process of creating candidate models and selecting the ones that perform best
    • Significant tasks left for the user:
      • Choosing a suitable business problem to be addressed by data mining.
      • Identifying and collecting data that is likely to contain the information needed to answer the business question.
      • Pre-processing the data so that the data mining tool can make use of it
      • Transforming the database so that the input variables needed by the model are available
      • Designing a plan of action based on the model and implementing it in the marketplace
      • Measuring results of the actions and feeding them back into the database for future mining.
  • 41. Hiring Outside Experts
    • Recommended approach if
      • Organization in early stages of integrating data mining in its business
      • Data mining activity is to be an one-off process
    • Not if
      • it is to be an ongoing process, eg, data mining for customer relationship management
    • Outside expertise for data mining is likely to be available in three possible places
  • 42. Hiring Outside Experts (cont’d)
    • Outside expertise for data mining is likely to be available in three possible places:
    • From a data mining software vendor
    • Data mining centres
    • Usually collaborations between universities and private companies
    • Eg, Monash Data Mining Centre
    • Consulting companies
    • Consulting company chosen should have had experience specifically in the area of interest to the organisation
  • 43. Developing In-house Expertise for data mining
    • Applies particularly to companies which have many products and customers
    • Should be a core competency of all large scale businesses
  • 44. Data Mining Development Methodology
    • Best practice yet to emerge (Hirji 2001)
    • A proposed a five-stage model (Cabena 1998):
      • Business objective determination
      • Data preparation
      • Data mining
      • Results analysis
      • Knowledge assimilation
  • 45. Data Mining Development Methodology (cont’d)
    • Cabena’s five-stage model:
    • Business objective determination
      • Clearly identifying the business problem to be mined
    • Data preparation
      • Data selection, preprocessing and transformation
    • Data mining
      • Algorithm selection and execution
    • Results analysis
      • Has anything new or interesting been found
    • Knowledge assimilation
      • Formulate ways of exploiting the new information extracted
  • 46. A Case Study (Hirji 2001)
    • Involved a large fast food outlet
    • Brought out some deficiencies of the above methodology
    • A new set of stages for data mining development and use proposed:
      • Business objective determination
      • Data preparation
      • Data audit
      • Interactive data mining and results analysis
      • Back end data mining
      • Results synthesis and presentation
  • 47. A Case Study (Hirji 2001) (cont’d)
    • Case study used IBM’s Intelligent Miner for Data on AIX as the data mining tool
    • Took 20 actual days of effort across the 6 stages above
    • Back end data mining involves data enrichment and additional data mining algorithm execution by the data mining specialist
    • Distribution of time required
    • 45% taken up by stages 4, 5 and 6
    • 30% required by the data preparation stage (70% predicted in the earlier model)
    • Use of a data warehouse saved time needed for selecting, cleaning, transforming, coding, and loading the data
  • 48. A Case Study (Hirji 2001) (cont’d)
    • Interactive data mining and results analysis stage
    • Linking data mining results with business strategy and using application software such as spreadsheets to perform sensitivity analysis of results obtained
    • Aims to demonstrate how data mining results support business strategy
  • 49. REFERENCES
    • Berry, M., & Linoff, G. Mastering Data Mining, Wiley Computer Publishing, New York 2000.
    • Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., and Zanasi, A. Discovering Data Mining: From Concept to Implementation . Prentice Hall, Englewood Cliffs, NJ 1998.
    • Dhar, V., & Stein, R .,”Deriving Rules from Data” in Seven Methods for Transforming Corporate Data into Business Intelligence ., Prentice Hall 1997, pp. 167-189, 251-258.
    • Ganti, V., Gehrke, J., & Ramakrishnan, R. Mining Very Large Databases , IEEE Computer, Vol.32 No.8, August 1999, pp.38-45.
    • Hirji, K., Exploring Data Mining Implementation , Communications of the ACM, Vol.44, No.7, July 2001, pp. 87-93.
    • Web site on Data Mining and Web Mining - http:// www.kdnuggets.com/software/suites.html