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COMP3740 CR32: Knowledge Management and Adaptive Systems

COMP3740 CR32: Knowledge Management and Adaptive Systems. Data Mining outputs: What “knowledge” can Data Mining learn? By Eric Atwell, School of Computing, University of Leeds (including re-use of teaching resources from other sources, esp. Knowledge Management by Stuart Roberts,

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COMP3740 CR32: Knowledge Management and Adaptive Systems

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  1. COMP3740 CR32:Knowledge Managementand Adaptive Systems Data Mining outputs: What “knowledge” can Data Mining learn? By Eric Atwell, School of Computing, University of Leeds (including re-use of teaching resources from other sources, esp. Knowledge Management by Stuart Roberts, School of Computing, University of Leeds)

  2. Data mining is about discovering “knowledge”: patterns, correlations, predictive rules in a large data-set or corpus. For this we need: Data mining techniques, algorithms, tools, eg WEKA, R, MatLab, … A methodological framework to guide us in collecting data and finding “useful” models, CRISP-DM Data Mining was originally about “learning” patterns from DataBases, data structured as Records, Fields Knowledge Discovery is “exotic term” for DM??? Increasingly, data is unstructured text (WWW), so Text Mining is a new subfield of DM/KD, focussing on Knowledge Discovery from unstructured text data Data Mining, Knowledge Discovery, Text Mining

  3. Data Mining: Overview Concepts, Instances, Attributes Data Mining Concept Descriptions Each instance is an example of the concept to be learned or described. The instance is described by the values of its attributes.

  4. Input to a data mining algorithm is in the form of a set of examples, or instances. Each instance is represented as a set of features or attributes. Usually this set takes the form of a flat file; each instance is a record in the file, each attribute is a field in the record. In text-mining, instance is word/term in a corpus. The concepts to be learned are formed from patterns discovered within the set of instances. instances

  5. The types of concepts we try to ‘learn’ include: Key “differences” between 2 (or more) data-sets – Eg difference in sales by region this year compared to previous - Eg terms important in one corpus but not another Clusters or ‘Natural’ partitions; Eg cluster customers according to their shopping habits; - Eg semantic clusters: “synonyms” with similar COLLOCATIONS Rules for classifying examples into pre-defined classes. Eg successful PhD student?: Mature student, IS, AI3n, 2i/1st => PhD Eg predicting Part-of-Speech word-class of each word in a corpus: Adj + X + Verb => X=Noun; “to” + X + Adverb => X=Verb concepts

  6. The types of concepts we try to ‘learn’ include: General Associations Eg “People who buy nappies are in general likely to also buy beer” - Eg high-frequency terms tend to be “grammatical”, not “meaningful” Numerical prediction Eg look for rules to predict what salary a graduate will get, given A level results, age, gender, programme of study and degree result – this may give us an equation: Salary = a*A-level + b*Age + c*Gender + d*Prog + e*Degree More concepts

  7. DB Example: weather to play?

  8. @relation weather @attribute outlook {sunny,overcast,rainy} @attribute temperature real @attribute humidity real @attribute windy {TRUE, FALSE} @attribute play {yes, no} @data sunny,85,85,FALSE,no sunny,80,90,TRUE,no overcast,83,86,FALSE,yes rainy,70,96,FALSE,yes rainy,68,80,FALSE,yes rainy,65,70,TRUE,no overcast,64,65,TRUE,yes sunny,72,95,FALSE,no sunny,69,70,FALSE,yes rainy,75,80,FALSE,yes /usr/local/weka-3-4-5/data/weather.arff

  9. Save records as plain text file, comma-separated values (csv format) Add HEADER: @relation <filename> @attribute <name><type> @attribute<name><type> … @data … then the data (instances) In general, any DB records can be ARFFed

  10. Concept-learning example Start with set of instances Use clustering algorithm to partition set

  11. Concept-learning example Identify cluster centroids

  12. Concept-learning example Clusters, represented by centroids are the learned concepts

  13. Point of sale data contains information about the buyer and the ‘basket’. We want to target advertising to different types of shopper. Cluster analysis groups shoppers into classes, each with distinctive characteristics. Cluster characteristics are examined to interpret what kind of advertising each group will respond to. Groups then related to where they live. Example use of clustering

  14. Output can take the form of: Classification of each instance according to the cluster number/name (like a dictionary/thesaurus) Cluster centroids Dendrogram depicting hierarchical partitioning: Output: Clusters x c f y d p o k a e m b l s

  15. Finding specialist terms, UK v US? Compare this month’s data with last month’s Compare with several previous months Notice new sales growth areas Trends – rise, fall, cyclical (eg turkey sales?) Key differences may denote clusters (eg ise/ize) “size/scale” of difference “Aligned”, “parallel” corpora used in Statistical Machine Translation, eg Google Translate Example use: comparing data-sets

  16. Key instances/attributes with most significant difference, eg highest Log-Likelihood score Groups or clusters of significant terms, eg names Trends over several data-sets: graphs Overall metrics of difference Output: differences between data-sets

  17. A large database of symptoms and diagnoses is available from medical records. We seek rules that will predict which disease someone has, given their symptoms. Or Given information about physical environment and crop yields – seek rules that will help us understand why some areas give higher yields than others. Example use of classifying

  18. Output: decision tree Outlook sunny rainy Humidity Windy high normal true false Play = ‘no’ Play = ‘yes’ Play = ‘no’ Play = ‘yes’

  19. Non-leaf node represents a test on a particular attribute. Arcs represent the outcomes of the test. Tests on numerical attributes usually have binary outcome Tests on nominal attributes usually have one outcome for each element in the domain. The leaf nodes represent a class. Each path down the tree represents a prediction for assigning instances to classes About decision trees

  20. Output: classification rules If outlook = sunny and humidity = high then play = no If outlook = rainy and windy = true then play = no If outlook = overcast then play = yes If humidity = normal then play = yes Default play = yes

  21. Alternative to decision trees: If <antecedent> then <consequent> Consequent indicates a class. Usually the antecedent is a conjunction of conditions on attribute values. Usually we interpret the set of rules to be a disjunction of the individual rules. Evaluation: Accuracy of a rule: Ratio of number of instances it predicts correctly to total number of instances that match the antecedent. Advantages of rules: Easier to read than trees Can be more compact Each rule represents a ‘nugget’ of knowledge, with its own accuracy About Classification rules

  22. General form: If A then B except if C then D Advantages: Can be more compact than rules without exceptions Closer to the way we organise our knowledge Scales well as new instances are introduced. A variant: rules with exceptions

  23. Given point of sales data, seek any kind of dependencies between data items that will help us understand shopping behaviour. “People who live by the sea and buy pet food go on fewer holidays” ‘Learned’ rules may or may not be interesting! Output: association rules

  24. Similar to classification rules, but now consequent can predict any attribute, not just the class. Evaluation: Coverage (or support) of a rule: The number of instances it correctly predicts Evaluation: Accuracy (or confidence) of a rule: Ratio of number of instances it predicts correctly to total number of instances that match the antecedent. About association rules

  25. Best-fit equation e.g.linear: length = a + b*width + c*height Widely used in maths and stats ?not “really” data mining? Output: numerical prediction

  26. Given numerical information about physical environment and crop yields – seek rules that will help us predict crop yields for some new set of conditions. Example use of numerical prediction

  27. Data Mining tools semi-automate the process of discovering patterns in data. Tools differ in terms of what concepts they discover (differences, clusters, decision-trees, rules, numerical prediction)… … and in terms of the output they provide (eg clustering algorithms provide a set of centroids or a dendrogram) Selecting the right tools for the job is based on business objectives: what is the USE for the knowledge discovered Key points

  28. You should be able to: Decide what attributes are relevant to the given data mining task Decide which is the appropriate data mining technique for a given a problem defined in terms of business objectives. Decide which is the most appropriate form of output. Self-test

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