KDD E A MINERAÇÃO DE DADOS. Daniela Barreiro Claro

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1 KDD E A MINERAÇÃO DE DADOS Daniela Barreiro Claro

2 Outline Introduction KDD Pré-Processamento Mineração de Dados Tarefas Pós-Processamento Prof. Daniela Barreiro Claro 2 de X;X=

3 BIG Data Huge amount of data Urgent necessity to have new techniques and tools automate the process to extract data These techniques and tools may help to transform this huge amount of data into relevant and useful information. Necessity is the mother of invention Data mining Automated analysis of huge amount of data sets. Prof. Daniela Barreiro Claro 3

4 BIG Data Large number of transactions is running each day, for instance: Walmart (Bompreço) Carrefour (Extra) Remote sensors Telecomunications networks Medical records, patients records, etc Prof. Daniela Barreiro Claro 4

5 BIG Data The World is Data Rich but information poor Collected data is being stored into large repositories. Data Tombs Tumbas de Dados Achieved data that is rarely visited Prof. Daniela Barreiro Claro 5

6 KDD Knowledge Discovery in Databases Data Knowledge Discovery process using data stored Following Fayyad 1996, KDD is: The nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data KDD has some steps: Selection, pre-processing (transformation), interpretation/evaluation and knowledge Prof. Daniela Barreiro Claro 6

7 KDD - Knowledge Discovery in Databases Prof. Daniela Barreiro Claro 7

8 KDD - Knowledge Discovery in Databases 1. Domain knowledge 2. Creating of the dataset 3. Pre-processing and Transformations 4. Choose of DM technique 5. Choose of DM algorithm 6. Interpretation and evaluation of patterns found 7. Knowledge discovery Prof. Daniela Barreiro Claro 8

9 KDD - Knowledge Discovery in Databases Some steps of KDD can be visualized as a Data Warehouse (DW) 9

10 KDD - Knowledge Discovery in Databases Three macro steps Pre- Processing Data Cleaning Data Integration Data Transformation Data Reduction Data Mining Techniques of DM Algorithms of DM Pos-Processing Analysis and evaluation of the patterns discovered Prof. Daniela Barreiro Claro 10

11 Pre-Processing Real data have normally the following characteristics: Incomplete Attributes are missing values, attributes are aggregate Wrong There are errors; attributes with unexpected values Inconsistence There are discrepancies among data items; some attributes that represent a concept, can have distinct names in different databases. Huge amount of data Large number of data makes data mining process very slow Prof. Daniela Barreiro Claro 11

12 Pre-Processing The pre-processing process can highlight 4 steps: Data Cleaning To clean the data To complete the data that is missing To resolve inconsistencies To soften error (suavizar) To eliminate or minimize discrepancies among data If data is dirty, therefore the results will be unreliable Prof. Daniela Barreiro Claro 12

13 Pre-Processing Data Integration Integrate the data from different databases, data cubes, file systems, etc Some attributes that represent a concept can have different names in different databases. Ex. IdCliente, ClienteID, Cli_ID, Some attributes can be inferred by others Ex. Annual salary, total amount Many times the data integration process can generate some redundancy. In this cases, the step Data Cleaning must be re-executed to eliminate the redundancy generated by this phase Prof. Daniela Barreiro Claro 13

14 Pre-Processing Data Transformation Esta fase envolve dois procedimentos principais Agregação Combinação de dois ou mais objetos em um único Ex. Agregar os 365 dias em 12 meses Mudança de escala Conjunto de dados menores requerem menos memoria e tempo de processamento Quantidades agregadas, como médias e totais tem menos variabilidade do que objetos individuais Desvantagem Perda de detalhes interessantes 14

15 Pre-Processing Data Transformation Normalization or Standardization Discrete data set has some properties If different variables need to be combined, it is necessary to transform them to avoid that large values dominate the results. Ex. Two variables: age and salary Difference between both values salary (thousands of dollars) and age (less than 130) Prof. Daniela Barreiro Claro 15

16 Pre-Processing Data Reduction Reduction of data representation considering volume, even if it produces the same analytical result (or similar). Strategies Aggregation To construct a data cube Attribute selection To eliminate irrelevant attributes by the use of a correlation analysis Dimension reduction Data discretization Prof. Daniela Barreiro Claro 16

17 Pre-Processing Data Reduction Dimension reduction A dimension consider the number of attributes Can eliminate irrelevant characteristics and noise reduction Can generate a more comprehensive model Can reduce data and many times examine them. Many times is used to join attributes generating new attributes, that is, a combination of old attributes Data Discretization Transforming a continuous attribute into a categorical attribute (discrete) or into binary attributes(binary process ) Prof. Daniela Barreiro Claro 17

18 Data Mining Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Alternative names Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, information harvesting, business intelligence, etc. Prof. Daniela Barreiro Claro 18

19 Data Mining Machine Learning Statistics Applications BI / Web Search Data Mining Database Technology Visualization Prof. Daniela Barreiro Claro 19

20 Data Mining It is one of the steps in a KDD process Two macro aims: Prediction Description Prediction To predicte values to future variable or not known variables. Description Discovery of patterns that describe the data set Prof. Daniela Barreiro Claro 20

21 Data Mining Data Mining Prediction Description Classification Regression Clustering Summarization Association THECHNIQUES Prof. Daniela Barreiro Claro 21

22 Supervised vs. Unsupervised Learning Supervised learning (prediction) Supervision: The training data (observations, measurements, etc.) are accompanied by labels indicating the class of the observations New data is classified based on the training set Unsupervised learning (description) The class labels of training data is unknown Given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters or associantions in the data 22

23 Data Mining Data Mining Prediction Description Classification Regression Clustering Summarization Association THECHNIQUES Prof. Daniela Barreiro Claro 23

24 Classification techniques Classification predicts categorical class labels (discrete or nominal) classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data Prof. Daniela Barreiro Claro 24

25 25 Classification A Two-Step Process Model construction: describing a set of predetermined classes Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute The set of tuples used for model construction is training set The model is represented as classification rules, decision trees, or mathematical formulae Model usage: for classifying future or unknown objects Estimate accuracy of the model The known label of test sample is compared with the classified result from the model Accuracy rate is the percentage of test set samples that are correctly classified by the model Test set is independent of training set (otherwise overfitting) If the accuracy is acceptable, use the model to classify new data Note: If the test set is used to select models, it is called validation (test) set

26 Prof. Daniela Barreiro Claro 26

27 10 10 Classification techniques Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No 10 No Small 90K Yes Training Set Tid Attrib1 Attrib2 Attrib3 Class 11 No Small 55K? 12 Yes Medium 80K? 13 Yes Large 110K? 14 No Small 95K? 15 No Large 67K? Test Set Induction Deduction Learning algorithm Learn Model Apply Model Model 27 de X

28 Classification algorithms Decision Tree based Methods Rule-based Methods Memory based reasoning Neural Networks Naïve Bayes and Bayesian Belief Networks Support Vector Machines FORMAS - UFBA 28 de X

29 10 Classification- Decision tree Splitting Attributes Tid Refund Marital Status Taxable Income 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No Cheat 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes Refund Yes No NO MarSt Single, Divorced TaxInc < 80K > 80K NO YES Married NO Training Data Model: Decision Tree

30 Classification- Decision tree Another example 10 Tid Refund Marital Status Taxable Income 1 Yes Single 125K No 2 No Married 100K No 3 No Single 70K No 4 Yes Married 120K No Cheat 5 No Divorced 95K Yes 6 No Married 60K No 7 Yes Divorced 220K No 8 No Single 85K Yes 9 No Married 75K No 10 No Single 90K Yes Married NO MarSt Yes NO Single, Divorced Refund TaxInc < 80K > 80K NO No YES There could be more than one tree that fits the same data!

31 10 10 Decision Tree Classification Task Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No Tree Induction algorithm 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes Induction 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No Learn Model 10 No Small 90K Yes Training Set Tid Attrib1 Attrib2 Attrib3 Class 11 No Small 55K? Apply Model Model Decision Tree 12 Yes Medium 80K? 13 Yes Large 110K? Deduction 14 No Small 95K? 15 No Large 67K? Test Set

32 10 Apply Model to Test Data Start from the root of tree. Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? NO MarSt Single, Divorced TaxInc < 80K > 80K Married NO NO YES

33 10 Apply Model to Test Data Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? NO MarSt Single, Divorced TaxInc < 80K > 80K Married NO NO YES

34 10 Apply Model to Test Data Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? NO MarSt Single, Divorced TaxInc < 80K > 80K Married NO NO YES

35 10 Apply Model to Test Data Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? NO MarSt Single, Divorced TaxInc < 80K > 80K Married NO NO YES

36 10 Apply Model to Test Data Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? NO MarSt Single, Divorced TaxInc < 80K > 80K Married NO NO YES

37 10 Apply Model to Test Data Test Data Refund Marital Status Taxable Income Cheat Yes Refund No No Married 80K? NO MarSt Single, Divorced Married Assign Cheat to No TaxInc NO < 80K > 80K NO YES

38 10 10 Decision Tree Classification Task Tid Attrib1 Attrib2 Attrib3 Class 1 Yes Large 125K No 2 No Medium 100K No Tree Induction algorithm 3 No Small 70K No 4 Yes Medium 120K No 5 No Large 95K Yes Induction 6 No Medium 60K No 7 Yes Large 220K No 8 No Small 85K Yes 9 No Medium 75K No Learn Model 10 No Small 90K Yes Training Set Tid Attrib1 Attrib2 Attrib3 Class 11 No Small 55K? 12 Yes Medium 80K? 13 Yes Large 110K? Deduction Apply Model Model Decision Tree 14 No Small 95K? 15 No Large 67K? Test Set

39 Classification- Decision tree 4 macro steps: 1. Divide training data set and test data set 2. Choose the classification attribute (labeled attribute) Decide what features of the data are relevant to the target class we want to predict. Verify the more relevant attribute (entropy and information gain) 3. Generate the decision tree 4. Test the efficiency of the classification algorithm using the test data set Prof. Daniela Barreiro Claro 39

40 Classification- Decision tree Entropy It is a measure of impurity. It is defined for a binary class with values a/b as: Entropy = - p(a)*log(p(a)) - p(b)*log(p(b)) Information gain It is usually a good measure for deciding the relevance of an attribute It is to define a preferred sequence of attributes to investigate to most rapidly narrow down the state of the predict class A notable problem occurs when information gain is applied to attributes that can take on a large number of distinct values One of the input attributes might be the customer's credit card number. This attribute has a high mutual information, 40 de X

41 Classification- Exercise 41

42 Classification- Decision tree - Results Using a Decision tree algorithm 42

43 Classification- Decision tree - Exercise Name gender Pedro Miguel Ana Gabriela M M F F Predict Daniela s genre? FORMAS - UFBA Features Ends vowel Number of vowel Lenght 43 de X

44 Regression techniques Represents a function to predict a number Can predict the height of a child given the child s age Linear regression is the most simple to use Algorithms examples GLM _ Generalized Linear Model Based on statistical techniques SVM Support Vector Machines Supports linear and non-linear regression Prof. Daniela Barreiro Claro 44

45 Data Mining Data Mining Prediction Description Classification Regression Clustering Summarization Association TECHNIQUES Prof. Daniela Barreiro Claro 45

46 Clustering techniques Mapping the data to a cluster Classes are determined by dataset (different from classification where labeled classes are pre-defined) Most used algorithm: K-means Determined the number of clusters (k) Values are random selected and included into each cluster; each value represents the centroid of each cluster Each point (value) is associated with a cluster which is more close to Close to is determined with the minor distance between a value and the centroid of each cluster. Ex. Cosine similarity 46

47 Clustering techniques When all values are analyzed, the centroid of each cluster is recalculate based on all values in each cluster New clusters are generated based on new centroid This process repeats until No value is reallocated anymore, that is, each value stays in each cluster Or the user define a finite number of iterations Prof. Daniela Barreiro Claro 47

48 Clustering - Exercise Considering the following dataset: A1(2,10) A2(2,5) A3(8,4) A4(5,8) A5(7,5) A6(6,4) A7(1,2) A8(4,9) Consider the following seeds(centroid) A1, A4, A7 Euclidian distance between the data values: 48

49 Clustering - Solution d(a,b) denotes the Euclidian distance between a e b Seed1=A1=(2,10); seed2=a4=(5,8), seed3=a7=(1,2) Euclidian distance can be obtained via the given matrix or by calculate: d(a,b)=sqrt((x b -x a ) 2 +(y b -y a ) 2 ) Prof. Daniela Barreiro Claro 49

50 Prof. Daniela Barreiro Claro 50 S O L U Ç Ã O

51 Values CentroID 1a iteration New centroid Prof. Daniela Barreiro Claro 51 S O L U Ç Ã O

52 Clustering Final solution 52

53 Association techniques Analysis of data which normally occur together, suggesting an association between them. Considering data value d1 -> d2 An association rule defines that if a data value d1 occurs, it is frequently that the data value d2 also occurs. Ex. If a client buy a bread, it is frequently that he buys also a butter The algorithm more used : A priori Prof. Daniela Barreiro Claro 53

54 Association techniques Support and Confidence Support It is the probability a transaction contains the frequency of implication A B Confidence It is the probability that a transaction contains A and also B (hard implication) Prof. Daniela Barreiro Claro 54

55 Association techniques Main definitions A set of frequent elements : a set of items with minimum support (L i for each i th element in a set). Apriori property Any subset of frequent items must be frequent. Join operation Find L k, a set of candidate items k generate by the join L k -1 with itself. Prof. Daniela Barreiro Claro 55

56 Association techniques Prof. Daniela Barreiro Claro 56

57 Association techniques Apriori Prof. Daniela Barreiro Claro 57 property

58 Association techniques And its association? This frequent data set will be used to generate association rules that satisfy both minimum support and confidence Taking S={2,3,5}, analyze all non empty subsets {2,3}, {2,5}, {3,5}, {2}, {3}, {5} Analyze each confidence between the set S and its subsets; Rule {2,3,5}/{2,3} = 2/2=100% Rule {2,3,5}/{2,5} = 2/3=67% - reject due to confidence 70% Prof. Daniela Barreiro Claro 58

59 Apriori algorithm - exercise Considering this dataset with 9 transactions Minimum support is the number of occurrence = 2 (min_sup = 2/9 = 22 %) Minimum confidence is 70%. Dataset k Rules found: Rule 1: I1 I5 I2 Rule 2: I2 I5 I1 Rule 3: I5 I1 I2 59

60 Pos-Processing Analyze retrieved information Generate knowledge In many times, this is a cyclic process, that is, it is necessary to redo in order to find useful information KDD is a slow process Prof. Daniela Barreiro Claro 60

61 Prof. Daniela Barreiro Claro Our course: Course id: MATE04 (2016.1) Semantic Applications and Formalisms Research Group /formasresearchgroup /formasresearch

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