AIMachine learning Neural networks Deductive detabases

Detecting regularities in data (bird flue cases) Detecting rare occurrences, rare events Finding “causal” relationships

Opportunities Collecting vast amounts of data has become possible. Ex1: Astromomy: petabytes of information are collected Laboratory for Cosmological Data Mining (LCDM) 1 petabyte (PB) = 2 50 bytes = 1,125,899,906,842,624 bytes. 1 petabyte = 1,024 terabytes 1 terabyte (TB) = 1,024 gigabytes => The armchair astronomer

Ex2: Biology: huge sequences of nucleotides have been collected. (The human genome contains more than 3.2 billion base pairs and more than genes). Very little of that has been interpreted yet.

Ex: Physics, Geography, weather data, … Business, … numerical discrete continuous categorical raw data cleaned data complete records Incomplete records (missing data) formatted data unformatted data

Tasks Fit data to model –Descriptive –Predictive Finding the “best” model ??? –Beware of model overfitting! Interpreting results Evaluating models (ex: lift charts) => Usually a lot of going back and forth between model(s) and data

Another complementary tack: Interactive visual data exploration Remarkable properties of the human visual system. (ex: analysis of a pseudo random number generator) Various visual representation schemes –Simultaneous viewing –(fast) sequential viewing Animating data (dynamic queries) Other possibilities: converting data to sounds, etc.

Two broad approaches to Learning Supervised learning ex: want to discover a model to help classify stars, based on emission spectra. In the “training set” the correct classification of the stars is known. The resulting model is used to predict the class of a new star (not in the training set) Unsupervised learning ex: want to group a set of stars into a small number sufficiently homogenous sub-groups of stars

Many techniques Fast evolving field Statistical –Descriptive stats, graphics,.. –Regression analysis –Principal components analysis –Time series analysis –Cluster analysis (use of a distance measure) –Naïve Bayse classifiers Artificial intelligence –Rule induction (Machine Learning) –Various inference techniques (various logics, deductive databases,…)

–Pattern matching (speech recognition) –Neural networks (many approaches) –Genetic algorithms –Baysian networks (probably the best approach to model complex causal structures) Information retrieval –Many specialized models (vector model,…) –Concepts of Precision and Recall Many ad hoc techniques –Co-occurrence analysis –MK generality analysis –Association analysis

One famous technique Ross Quinlan’s ID3 algorithm

The weather data ObjectOutlookTemperatureHumidityWindyClass 1sunnyhothighFALSEN 2sunnyhothighTRUEN 3overcasthothighFALSEP 4rainmildhighFALSEP 5raincoolnormalFALSEP 6raincoolnormalTRUEN 7overcastcoolnormalTRUEP 8sunnymildhighFALSEN 9sunnycoolnormalFALSEP 10rainmildnormalFALSEP 11sunnymildnormalTRUEP 12overcastmildhighTRUEP 13overcasthotnormalFALSEP 14rainmildhighTRUEN

From decision trees to rules Reading rules from a tree –Unambiguous –Rule order not counting –Alternative rules for the same conclusion are ORed –But too complex rules

Rules can be much more compact than trees Ex: if x=1 and y = 1 then class=a if z=1and w=1 then class=a Otherwise class=b

From rules to decision trees Rule disjunction result in too complex trees. Ex: write as a tree –If a and b then x –If c and d then x (Fig. 3.2)Fig. 3.2 (replicated sub-tree problem) Ex: tree and rules of equivalent complexityEx Ex: tree much more complex than rules

To learn from examples, the examples must be rich enough Ex: sister-of relation (fig 2-1)fig 2-1 Denormalization (fig 2-3)fig 2-3 Importance of data preparation

Attributes An attribute may be irrelevant in a given context (ex: number of wheels for a ship in a database of transportation vehicles => Create value “irrelevant”

Software tools Many commercial software –CART ( –SPSS modules –WEKA (free) ( –For a larger list: Many field specific software –In the context of GRID computing Demonstrating WEKA

Ad hoc methods Co-occurrence analysis MK generality analysisgenerality analysis

Term Co-occurrence Analysis The following approach measures the strength of association between a term i and a term j of the set of documents by: e(i,j) 2 = (C ij ) 2 /(C i * C j ) Where: Ci : is the number of documents indexed by term i Cj : is the number of documents indexed by term j Cij : is the number of documents indexed both by terms i and j

Interactive Data Visualization Fish eye views Hyperbolic trees Linear Visual data sequences Dynamic queries

Tree Maps Financial Data Data

Conclusion Current state of the art (Graphic Models – Markov networks) Still an art Ethical issues

Baysian Networks Objective: determine probability estimates that a given sample belongs to a class Probability(x  Class | attribute values) Baysian network: –One node for each attribute –Nodes connected in an acyclic graph –Conditional independance

Learning a baysian network from data Function for evaluating a given network based on the data Function for searching through the space of possible networks K1 and TAN algorithms

Baysian Networks   Graphical Models = Markov models undirected edges