Folie 1 - uni-luebeck.de

Folie 1 - uni-luebeck.de

Web-Mining Agents Prof. Dr. Ralf Mller Universitt zu Lbeck Institut fr Informationssysteme Karsten Martiny (bungen) Organizational Issues: Assignments Start: Wed, 21.10., 2-4pm, AMHZ S1, Class also Thu 2-4pm, IFIS 2035 Lab: Fr. 2-4pm, Building 64, Inst. Math., Seminar room Hilbert (3rd floor) (registration via Moodle right after this class) Assignments provided via Moodle after class on

Thu. Submission of solutions by Wed 2pm, small kitchen IFIS (one week after provision of assignments) Work on assignments can/should be done in groups of 2 (pls. indicate name and group on submitted 2 Organizational Issues: Exam Registration in class required to be able to participate in oral exam at the end of the semester (2 slots)

Prerequisite to participate in exam: 50% of all points of the assignments 3 Search Engines: State of the Art Input: Strings (typed or via audio), images, ... Public services: Links to web pages plus mini synopses via GUI Presentations of structured information via GUI excerpts from the Knowledge Vault http://videolectures.net/kdd2014_murphy_knowledge_va ult/

(previously known as Knowledge Graph) NSA services: ? Methods: Information retrieval, machine learning Data: Grabbed from free resources (win-win suggested) 4 Search Results Web Results

have not changed Search Results This is whats new Map General

info Upcoming Events Points of interest *The type of information that appears in this panel depends on what you are searching for

Search Engines: State of the Art Input: Strings (typed or via audio), images, ... Public services: Links to web pages plus mini synopses via GUI Presentations of structured information via GUI excerpts from the Knowledge Vault (previously known as Knowledge Graph) NSA services: ? Methods: Information retrieval, machine learning Data: Grabbed from many resources (win-win suggested): Web, Wikipedia (DBpedia, Wikidata, ), DBLP,

Freebase, ... 7 Search Engines Find documents: Papers, articles, presentations, ... Extremely cool But Hardly any support for interpreting documents w.r.t. certain goals (Knowledge Vault is just a start) No support for interpreting data

Claim: Standard search engines provide services but copy documents (and possibly data) Why cant individuals provide similar services on their document collections and data? 8 Personalized Information Engines Keep data, provide information Invite agents to view (i.e., interpret) local documents and data, without giving away all data Let agents take away their interpretation of

local documents and data (just like in a reference library). Doc/data provider benefits from other agents by (automatically) interacting with them Agents should be provided with incentives to have them share their interpretations No GUI-based interaction, but semantic interaction via agents 9 [email protected] Web and Data Science

Module: Web-Mining Agents Machine Learning / Data Mining (Wednesdays) Agents / Information Retrieval (Thursdays) Requirements: Algorithms and Data Structures, Logics, Databases, Linear Algebra and Discrete Structures, Stochastics Module: Foundations of Ontologies and Databases Web-based Information Systems Data Management Mobile and Distributed Databases Semantic Web

10 Complementary [email protected] Algorithmics, Logics, and Complexity Signal Processing / Computer Vision Machine Learning Pattern Recognition Artificial Neural Networks (Deep Learning)

11 Web-Mining Agents Data Mining Prof. Dr. Ralf Mller Universitt zu Lbeck Institut fr Informationssysteme Karsten Martiny (bungen) Literature Stuart Russell, Peter Norvig, Artificial Intelligence A Modern Approach, Pearson, 2009 (or 2003 ed.)

Ian H. Witten, Eibe Frank, Mark A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2011 Ethem Alpaydin, Introduction to Machine Learning, MIT Press, 2009 Numerous additional books, presentations, and videos 13 Why Learn ? Machine learning is programming computers to optimize a performance criterion using example data or past experience Simple form of data interpretation

There is no need to learn to calculate payrolls Learning is used when: Human expertise does not exist (navigating on planet X), Humans are unable to explain their expertise (speech recognition) Solution changes in time (routing on a computer network) Solution needs to be adapted to particular cases 14

What We Talk About When We Talk About Learning Learning general models from data of particular examples Data might be cheap and abundant: Data warehouse (data mart) maintained by company Example in retail: Customer transactions to consumer behavior: People who bought Da Vinci Code also bought The Five People You Meet in Heaven (www.amazon.com) Build a model that is a good and useful

approximation to the data 15 Data Mining Application of machine learning methods to large databases is called Data mining. Retail: Market basket analysis, customer relationship management (CRM, also relevant for wholesale) Finance: Credit scoring, fraud detection Manufacturing: Optimization, troubleshooting Medicine: Medical diagnosis Telecommunications: Quality of service optimization

Bioinformatics: Sequence or structural motifs, alignment Web mining: Search engines 16 What is Machine Learning? Optimize a performance criterion using example data or past experience. Role of Statistics: Building mathematical models, core task is inference from a sample Role of Computer Science: Efficient algorithms to Solve the optimization problem

Representing and evaluating the model for inference 17 Sample of ML Applications Learning Associations Supervised Learning Classification Regression Unsupervised Learning Reinforcement Learning

18 Learning Associations Basket analysis: P (Y | X ) probability that somebody who buys X also buys Y where X and Y are products/services. Example: P ( chips | beer ) = 0.7 If we know more about customers or make a distinction among them: P (Y | X, D ) where D is the customer profile (age, gender, marital status, ) In case of a web portal, items correspond to

links to be shown/prepared/downloaded in advance 19 Classification Example: Credit scoring Differentiating between lowrisk and highrisk customers from their income and savings Discriminant: IF income > 1 AND savings > 2

THEN low-risk ELSE high-risk 20 Classification: Applications Aka Pattern recognition Character recognition: Different handwriting styles. Face recognition: Pose, lighting, occlusion (glasses, beard), make-up, hair style Speech recognition: Temporal dependency Use of a dictionary for the syntax of the language Sensor fusion: Combine multiple modalities; eg, visual (lip image) and acoustic for speech

Medical diagnosis: From symptoms to illnesses Brainwave understanding: From signals to states of thought Reading text: ... 21 Character Recognition 22 Face Recognition

Training examples of a person Test images AT&T Laboratories, Cambridge UK 23 24 Medical diagnosis 25 26

27 Regression Example: Price of a used plane x : plane attribute y : price y = g (x | ) g ( ) model, parameters y = wx+w0

28 Supervised Learning: Uses Prediction of future cases: Use the rule to predict the output for future inputs Knowledge extraction: The rule is easy to understand Compression: The rule is simpler than the data it explains Outlier detection: Exceptions that are not covered by the rule, e.g., fraud 29

Unsupervised Learning Learning what normally happens No output (we do not know the right answer) Clustering: Grouping similar instances Example applications Customer segmentation in CRM Company may have different marketing approaches for different groupings of customers

Image compression: Color quantization Instead of using 24 bits to represent 16 million colors, reduce to 6 bits and 64 colors, if the image only uses those 64 colors Bioinformatics: Learning motifs (sequences of amino acids in proteins) Document classification in unknown domains 30 Reinforcement Learning Learning a policy: A sequence of actions/outputs

No supervised output but delayed reward Credit assignment problem Game playing Robot in a maze Multiple agents, partial observability, ... 31 An Extended Example Sorting incoming Fish on a conveyor according to species using optical sensing Sea bass (cheap) Species Salmon

(expensive) 32 Problem Analysis Set up a camera and take some sample images to extract features Length

Lightness Width Number and shape of fins Position of the mouth, etc This is the set of all suggested features to explore for use in our classifier! 33 Preprocessing Use a segmentation operation to isolate fishes from one another and from the background Information from a single fish is sent to a feature

extractor whose purpose is to reduce the data by measuring certain features The features are passed to a classifier 34 35 Classification Now we need (expert) information to find features that enables us to distinguish the species. Select the length of the fish as a possible feature for discrimination

36 37 The length is a poor feature alone! Cost of decision Select the lightness as a possible feature. 38 39 Threshold decision boundary and cost relationship

Move our decision boundary toward smaller values of lightness in order to minimize the cost (reduce the number of sea bass that are classified salmon!) Task of decision theory 40 Adopt the lightness and add the width of the fish Fish xT = [x1, x2] Lightness

Width 41 42 We might add other features that are not correlated with the ones we already have. Precaution should be taken not to reduce the performance by adding such noisy features Ideally, the best decision boundary should be the one which provides an optimal performance

43 44 However, our satisfaction is premature because the central aim of designing a classifier is to correctly classify novel input Issue of generalization! 45 46

Standard data mining life cycle It is an iterative process with phase dependencies Consists of six (6) phases: 47 Phases (1) Business Understanding Understand project objectives and requirements Formulation of a data mining problem definition Data Understanding

Data collection Evaluate the quality of the data Perform exploratory data analysis Data Preparation Clean, prepare, integrate, and transform the data Select appropriate attributes and variables 48 Phases (2) Modeling Select and apply appropriate modeling techniques

Calibrate/learn model parameters to optimize results If necessary, return to data preparation phase to satisfy model's data format Evaluation Determine if model satisfies objectives set in phase 1 Identify business issues that have not been addressed Deployment Organize and present the model to the user

49 Fallacies of Data Mining (1) Fallacy 1: There are data mining tools that automatically find the answers to our problem Reality: There are no automatic tools that will solve your problems while you wait Fallacy 2: The DM process requires little human intervention Reality: The DM process require human intervention in all its phases, including updating and evaluating the model by human experts

Fallacy 3: Data mining have a quick ROI Reality: It depends on the startup costs, personnel costs, data source costs, and so on 50 Fallacies of Data Mining (2) Fallacy 4: DM tools are easy to use Reality: Analysts must be familiar with the model Fallacy 5: DM will identify the causes to the business problem Reality: DM tools only identify patterns in your data, analysts must identify the cause

Fallacy 6: Data mining will clean up a data repository automatically Reality: Sequence of transformation tasks must be defined by an analysts during early DM phases 51 Remember Problems suitable for Data Mining: Require to discover knowledge to make right decisions Current solutions are not adequate Expected high-payoff for the right decisions

Have accessible, sufficient, and relevant data Have a changing environment IMPORTANT: ENSURE privacy if personal data is used! Not every data mining application is successful! 52 Overview Supervised Learning 53

Learning a Class from Examples Class C of a family car Prediction: Is car x a family car? Knowledge extraction: What do people expect from a family car? Output: Positive (+) and negative () examples Input representation: x1: price, x2 : engine power 54

Training set X X {xt ,r t }tN1 1 if x is positive r 0 if x is negative x1 x x2 55

Class C p1 price p2 AND e1 engine power e2 56 Hypothesis class H 1 if h classifies x as positive h (x) 0 if h classifies x as negative Error of h on H N

E(h|X ) (1 / N) h xt r t t1 (a b) = 1 if , 0 otherwise 57 S, G, and the Version Space most specific hypothesis, S

most general hypothesis, G h H, between S and G is consistent and make up the version space (Mitchell, 1997) 58 Noise and Model Complexity Use the simpler one because

Simpler to use (lower computational complexity) Easier to train (lower space complexity) Easier to explain (more interpretable) Generalizes better (lower variance - Occams razor) 59 Multiple Classes, Ci i=1,...,K X {xt ,r t }tN1

t 1 if x Ci t ri t 0 if x C j , j i

Train hypoth hi(x), i =1,.. t 1 if x Ci t hi x

t 0 if x C j , j i 60 Regression t X x ,r

t N t 1 rt g x w1x w 0 r t f xt 2

1 N t E g | X r g xt N g x w 2x 2 w1x w 0 t 1 1 E w1 , w0 | X N N

r w x t 1 t w0 2 t 1

ial derivatives of E w.r.t w1 and w0 and setting them to 0 -> minimize error t t w1 x r xr N t t 2

(x ) Nx 2 t w0 r w1 x 61 Model Selection & Generalization Learning is an ill-posed problem;

data is not sufficient to find a unique solution The need for inductive bias, assumptions about H Generalization: How well a model performs on new data Overfitting: H more complex than C or f Underfitting: H less complex than C or f 62 Triple Trade-Off There is a trade-off between three factors (Dietterich, 2003): 1. Complexity of H, c (H),

2. Training set size, N, 3. Generalization error, E, on new data As N, E As c (H), first E and then E 63 Cross-Validation To estimate generalization error, we need data unseen during training. We split the data as Training set (50%) Validation set (25%)

Test (publication) set (25%) Resampling when there is few data 64 Dimensions of a Supervised Learner 1. Model: g x |

t t E | X L r , g

x | 2. Loss function: t 3. Optimization procedure: * arg min E | X

65 66

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