Data Mining - The Big Picture!

| © Copyright 2015 Hitachi Consulting1
Data Mining
The big picture!
Khalid M. Salama, Ph.D.
Microsoft Business Intelligence
Hitachi Consulting UK
We Make it Happen. Better.

Outline
Context
Data Mining Tasks, Techniques, and Applications
Knowledge Discovery Process
Screenshots
Concluding Remarks

Business Intelligence as a Context
Business Intelligence - “A broad category of concepts, methods, tools and
techniques of collecting, storing, managing, analysing and sharing data to
support/improve decision making”.
 Data Mining is a subset of these concepts, methods, tools and techniques that
concerns with automatically extracting hidden, useful patterns from the data.
 Examples:
−CRM: Customer Segmentation, Profiling, etc.
−Finance, Banking & Insurance: Fraud Detection, Credit Scoring, Stock Market, etc.
−Medicine/Health Care: Disease Development, Diagnosis, Best Treatments, etc.
−Telecommunication: Churn Analysis, Network Fault Isolation, etc.
−Retail: Cross-selling, Targeted Marketing, Propensity Modelling, etc.
revealing the mystery…

Terms and Significance
 Data Mining – “An interdisciplinary subfield of computer science, which is the computational
process of discovering patterns in datasets” – “Knowledge Discovery in Databases (KDD)”
 Data Science – “the extraction of knowledge from volumes of data, which is a continuation of
the field data mining and predictive analytics”
 Machine Learning – “A subfield of computer science that evolved from the study of pattern
recognition and computational learning theory”
 Predictive Analytics – “A variety of statistical techniques from modelling, machine learning,
and data mining that analyse current and historical facts to make predictions about future”
 Big Data – “A broad term for data sets so large or complex that traditional data processing
applications are inadequate”
brining order to buzzwords chaos

Data Mining
… in a nutshell
Data
Mining
Machine
Learning
Statistics
Artificial
Intelligence
Databases
Other
Technologies
“Data mining, an interdisciplinary subfield of
computer science, is the computational
process of discovering patterns in large data
sets involving methods at the intersection of
artificial intelligence, machine learning,
statistics, and database systems.”
Other Related Technologies:
 Visualization
 Big Data
 High Performance Computing
 Cloud Computing
 Others..

Knowledge Discovery in Databases (KDD)
…or data science, if you like!
Understanding
the Business
Understanding
the Data
Preparing
the Data
Modelling
Evaluation -
InterpretationDeployment
Cross Industry Standard Process
for Data Mining (CRISP-DM)
Data

Data Mining Taxonomy
A 10,000 foot view…
Learning Paradigms
Mining Tasks
Modelling Techniques
Measures
Heuristic Search Methods
Supervised Learning
Classification
Decision Trees
Information Gain
Greedy Recursive Partitioning

Learning Paradigms
Data as the teacher, machine as the student…
Supervised Learning
Labelled data = data + output (predictable, target, response, class) variable
Learn the relationship between data and output
Unsupervised Learning
Unlabelled data
Learn associations, similarities, groups, etc.
Semi-
supervised
Learning
Partially labelled data
Online/Active
Learning
Real-time learning on
data streams
Reinforcement Learning
game theory, control theory, simulation-based
optimization, operations research, robotics, etc.

Data Mining Task
…only the genuine ones!
• Predicting the class of a given case – SupervisedClassification
• Estimating the value of a response variable – SupervisedRegression
• Partitioning the cases into similar groups – UnsupervisedClustering
• Finding frequent (co)-occurring items – UnsupervisedAssociation Rules Analysis
• Finding similar cases of a given case – BothSimilarity Analysis
• Calculating the probability of variables – BothProbabilistic Inference
• Forecasting future values – SupervisedTime Series Analysis
Important Terms:
• Learning Paradigms:
− Supervised
− Unsupervised
− Semi-supervised
− Others (Reinforcement
learning, Active, etc.)
• Analytics Types:
− Descriptive (Exploratory)
− Predictive
− Prescriptive (Decisive)
Application Fields:
• Text Mining
• Information Retrieval
• (Social) Web Mining
• Speech Recognition
• Image Recognition
• Anomaly Detection
• State Transition Analysis
• Collaborative Filtering
(Recommender systems)

Classification Learning
my favourite data mining task!
Data Mining Task:
• Classification
• Regression
• Clustering
• Association Rules Analysis
• Similarity Analysis
• Probabilistic Inference
• Time Series Analysis
Target Class Type
• Binary vs. Multi-class
• Multi-label
• Hierarchical Class
Classification Applications:
• Targeted Advertising
• Churn Analysis
• Fraud Detection
• OCR
• Sentiment Analysis
• Predictive Maintenance
• Document Classification
• Protein Function Prediction
• Medical Support Systems
 Input: Labelled cases (nominal labels).
 Process: Learn the relationships between the input variables and the target class.
 Output: A model that used to predicted the class of unlabeled cases (+ probability).
Model
(Classifier)
Classification
Algorithm
Outlook Temperature Humidity Windy Class
sunny hot high no Don’t
sunny hot high yes Don’t
overcast hot high no OK
rain mild high no OK
rain cool normal no OK
rain cool normal yes Don’t
overcast cool normal yes OK
sunny mild normal no Don’t
sunny cool normal no OK
rain mild normal no OK
sunny mild normal yes OK
overcast mild high yes OK
overcast hot normal no OK
rain mild high yes Don’t
OK
Labeled cases (Training Set)
Unlabeled (new) Case

Classification Learning
classification modelling techniques
Data Mining Task:
• Classification
• Regression
• Clustering
Classification Techniques:
• Decision Trees
• Classification Rules
• Linear Discriminant Analysis
• Artificial Neural Networks
• Instance-based Learning
• Probabilistic Graphical Models
• Support Vector Machines
• Gaussian Process
• Ensemble Methods
Advances Classification Task:
• Multi-label Classification
• Hierarchical Classification
 Decision Trees
 Forests/ Jungles
 Classification Rules
 Ordered List/ Unordered Set
 Linear Discriminate Analysis
 Logistic Regression
 Artificial Neural Networks
 Feed-forward Multilayer perceptron
 Instance-based Learning
 Nearest-neighbours classifiers
 Probabilistic Graphical Models
 Bayesian Network Classifiers
 Support Vector Machines
 Kernel Methods
 Gaussian Process
 Non-parametric Methods
 Ensemble Methods
 Bagging/ Boosting/ Stacking
IF .. AND .. AND .. THEN A
ELSE IF .. AND .. THEN C
ELSE IF .. AND .. THEN B
..
..
ELSE C
.
.
.

Regression Analysis
 Input: cases with numerical target variable (response value)
 Process: Learn the relationship: 𝑦 = 𝑓(𝐗).
 Output: A regression model that used to estimate the target value of new cases (+ confidence Intervals)
Linear Regression
 Simple Linear Regression: 𝑦 = 𝑎𝑥 + 𝑏
 Multi-variate Linear Regression: 𝑦 = 𝑎1 𝑥1 + 𝑎1 𝑥1 + ⋯ + 𝑎 𝑚 𝑥 𝑚 + 𝑏
 Generalized Linear Model (Binomial, Poisson, Chi-square, Gaussian, etc.)
Non-linear Regression
 Non-linear Transformation 𝑦 = 𝑎1 𝑙𝑜𝑔 𝑥1 + 𝑎2 𝑥2
3
+ 𝑏
 Multi-variate Adaptive Regression Splines (MARS)
 Regression Trees (Hierarchical Regression)
 Artificial Neural Networks
 Gaussian Process
Related Concepts
 Parameter Estimation: Least Square Error, Weighted LSE, ect.
 Regularization: least absolute shrinkage and selection operator (LASSO) - Ridge
 Model Selection (e.g. stepwise, Information Criterion, …)
the most classical ML task
Data Mining Task:
• Classification
• Regression
• Clustering
Regression Applications:
• Credit Scoring
• Survival Analysis
• Risk Estimation
• Value Evaluation
Regression Techniques:
• Simple vs. Multi-variate
• Generalized LM
• Local Models - Splines
• Trees - ANN - GP
Related Concepts:
• Parameter Estimation
• Regularization
• Model Selection

Cluster Analysis
 Input: cases without a specific target class.
 Process: find groups where the distance “within” is minimized, and the “between” in maximized.
 Output: case-cluster assignment (membership).
Clustering Techniques
 Exclusive vs. Overlapping
 K-Means vs. Fuzzy K-Means, EM
 Partitioned vs. Hierarchical
 K-Means vs. Agglomerative/Divisive
 Center-based vs. Density-based
 K-Means vs. DBScan
 Complete vs. Partial.
Clusters Quality
 Minimize intra-distance/linkage (Cohesion)
 Maximize inter-distance/linkage (Separation)
 Number of Clusters
… rather a mean to an end
Data Mining Task:
• Classification
• Regression
• Clustering
Clustering Applications:
• Customer Segmentation
• Outlier Detection
• Topic Grouping
• Profiling
• Summarisation
• Mixture of Models
Clustering Techniques
• Exclusive vs. Overlapping
• Partitioned vs. Hierarchical
• Center-based vs. Density-based
• Complete vs. Partial.

Association Rule Analysis
 Input: cases without a specific target class (or basket data).
 Process: Find frequent co-occurrences between variable values (items).
 Output: Frequent Item sets/ Association Rules.
 Frequent Item set: {a}, {b}, {d}, {ab} ,{ad}
 Association Rule: IF {a,b} THEN {d,e}
Approach
 Define Constrains (Data Space/ Rule Space)
 Frequent Item Generation (min. support threshold)
𝑠𝑢𝑝𝑝𝑜𝑟𝑡 𝑎, 𝑏 =
|𝑎,𝑏|
|𝑇|
 Rule Generation (min. confidence threshold).
𝑐𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 𝑎→𝑏 =
|𝑎,𝑏|
|𝑎|
 Prune and proceed to larger item set (adjusted thresholds)
 Rank the rules based on an interestingness measure
discovery of “interesting” relationships
Data Mining Task:
• Classification
• Regression
• Clustering
Asso. Rules Applications:
• Market Basket Analysis
• Text Mining - Sentiment Analysis
• Graph/Link Analysis
Rule Measures:
• Support & Confidence
• Interestingness
− Lift & Chi-Squared
− Jaccard & Kulczynzki
− Kappa & Conviction
Related Issues:
• Negative Item sets
• Quantitative Items
• Sequential Patterns
• Item Sets Compression
• Redundancy-Aware Patterns
• Colossal Item Sets & Scalability
a b c d e
T1 yes no yes yes no
T2 yes no no yes no
T3 no yes no no yes
.. .. .. .. .. ..
Basket Data
T1 → {a,c,d}
T2 → {a,d}
T3 → {b,e}
…

Similarity Analysis
a.k.a. instance-based learning
Data Mining Task:
• Classification
• Regression
• Clustering
Similarity Matching Applications:
• Case-based Reasoning
• Lazy Classification
• Record Matching
• Outlier Detection
• Search Engines
Attribute Proximity Measures :
• Edit-based – Levenstein and Jaro-
Winkler distance.
• Token-based – Jaccard, Shannon,
and Cosine Similarity.
• Sequence-based –Longest
Common Subsequence.
• Phonetic-based – Soundex and
Metaphone.
• Numeric-based – Euclidean
distance.
Casei Vi,1 Vi,2 … vi,m
Casej Vj,1 Vj,2 … vj,m
Weights W1 W2 … Wm
Att-1 Att-2 … Att-m
Similarity(i,j) = Sim(Vi,1,Vj,1 ) + Sim(Vi,2,Vj,2) … + Sim(Vi,m,Vj,m )W1 . W2 . Wm .
 Input: A set of (labelled/ unlabeled) cases + subject case.
 Process: find a set of similar cases to the subject case.
 Output: similar cases (nearest neighbors).
Proximity Measure
 Distance vs. Similarity
Weighting
 User Input vs.
Automatic Optimisation
Neighbours
 Distance-based (Threshold) vs. Top K
Classification / Regression
 Voting / Average
 Weighted Voting / Weighted Average – Kernel Methods (Gaussian Kernel)

Probability Estimation and Inference
 Input: A set of (labelled/ unlabeled) cases.
 Process: learn the structure/parameters of the variable dependency relationships
 Output: A Probabilistic Graphical Model
Probabilistic Graphical Models
 Directed Acyclic Graphs
 Bayesian Networks (classifiers)
 Dynamic Bayesian Networks
 Markov Blankets
 Directed Cyclic Graphs
 Markov Chains
 (Hidden) Markov Models
 Undirected Graphs
 Factor Graphs
 Dependency Networks
 Markov Random fields
Learning
 Structure (variable-dependency relationships)
 Parameters (quantification of the relationships)
Inferencing
 Exact inference and the junction tree
 MCMC
 Variational methods and EM
the doctrine of chances…
Data Mining Task:
• Classification
• Regression
• Clustering
Probabilistic Inference
Applications:
• ML Framework
• Diagnostic Systems
• State Transition Analysis
Probabilistic Graphical Models:
• Directed Acyclic Graphs
− Bayesian Networks
− Markov Blankets
• Directed Cyclic Graphs
− Markov Chains
− Markov Models
• Undirected Graphs
− Factor Graphs
− Dependency Networks
− Markov Random fields

Time Series Analysis
 Input: a sequence of evenly-spaced numerical data.
 Process: learn a function that describe the current value with respect to the previous ones.
 Output: Time Series Model (describe/forecast).
Components:
 Trend: Overall upward, downward, or stationary pattern.
 Cyclical: Repeating upwards or downwards movements.
 Seasonal: Regular pattern of up & down fluctuations.
 Irregular: Unsystematic, ‘residual’ fluctuations (random).
Techniques:
 Regression.
 (Weighted) Moving Average.
 Exponential Smoothing.
 Auto-regressive (STL, ARMA, ARIMA, etc.).
history tends to repeat itself…
Data Mining Task:
• Classification
• Regression
• Clustering
Time Series Applications:
• Stock Market
• Supply/Demand
• Financial Applications
• Signal Processing
Time Series Components
• Trend
• Cyclical
• Seasonal
• Random
Techniques
• Regression
• Moving Average
• Exponential Smoothing
• Auto-regressive

Knowledge Discovery in Databases (KDD)
the virtuous cycle of data science
CRISP-DM Process:
• Understanding the Business
• Understanding the Data
• Preparing the Data
• Modelling
• Evaluation & Interpretation
• DeploymentUnderstanding
the Business
Understanding
the Data
Preparing
the Data
Modelling
Evaluation -
InterpretationDeployment
Data

Step 1 - Understanding the Business
Ways to answer “Data Analysis” questions:
 Query/Report – “How many new customers bought my service this month? How many renewed? How
many left?”
 Complex Query/Report – “What are the top selling products by region in the Online sales? How does
that compare to the store sales?” (Multi-dimensional Analysis/Visualisation)
 Calculations/KPIs – “Is my business going well? Are we meeting our targets?”
 What-if Analysis – “Based on the last year sales, what will be the revenues if we increase the price of
this product X by 1% and decreased the price of product Y by 2%?” (budgeting/planning)
 Statistical Analysis – “What are the most important factors that impact the energy consumption in our
facilities?” (dependency/correlation)
 Hypothesis Testing – “Is there significant improved amongst the group of people who took the new
drug, compared the placebo group?” (experimental studies/market research)
 Data Mining – “Who are the customer that most likely to response to our new advertising campaign?”
(predictive analytics)
“The formulation of a problem is often more essential than its solution” - Albert Einstein
CRISP-DM Process:
• Modelling
• Deployment
Analytics Techniques:
• Database Query
• Multi-dimensional
Analysis/Visualisation
• Calculations/KPIs
• What-if Analysis
• Statistical Analysis
• Hypothesis Testing
• Data Mining

Step 1 - Understanding the Business
 A business problem can be decomposed into multiple business question, which of each can
be mapped to different analytics technique or data mining task.
 Example 1: Microsoft How-old.net
− “What are the distinct object in the picture?” → Clustering
− “For each object, is it a face or not?” → Classification
− “What is the estimate age for each identified face?” → Regression
 Example 2: Churn Analysis and Targeted Offering
− “Which customers would likely terminate the contract this month?” → Classification
− “Which service package will a customer likely purchase if given incentive ?” → Classification
− “How much will this customer use the service?” → Regression
− “What will be the expected utility of targeting this customer?” → Calculation
 Example 3: Planning
− “What will be the amount of demand on each item next year, per region?”→ Time Series
− “What will be the revenue according to this pricing schemes?” → What-if
from business problems to analytics tasks
CRISP-DM Process:
• Modelling
• Deployment
Analytics Techniques:
• Database Query
• Multi-dimensional
Analysis/Visualisation
• Calculations/KPIs
• What-if Analysis
• Statistical Analysis
• Hypothesis Testing
• Data Mining

Step 2 – Understanding the Data
what is data?
CRISP-DM Process:
• Modelling
• Deployment
“Data are values of qualitative or quantitative variables, belonging to a set of items”
Variables
 Numerical
 Categorical (Nominal, Ordinal)
 Special (Identifier, Time Index)
What should data look like:
 One row for each case
 Columns represent attributes
What does data really look like:
 Transactional (normalised) data
 Ordered data
− Sequence data (DNA)
− Time-based data (temporal auto-correlated)
− Spatial data (spatial auto-correlated)
 Graph-based data
 Free-from Text
 Image/Video (sequence of images)
 Audio
Id Att-1 Att-2 .. Att-M
Case 1 V(1,1) V(1,2)
Case 2 V(2,1) V(2,2)
…
Case N V(N,M)
Variables:
• Numerical
• Categorical
− Nominal
− Ordinal
Data Forms:
• Matrix
• Normalized
• Ordered
− Sequence
− Time-Series
− Spatial
• Graph-based
• Free-from Text
• Image/Video
• Audio

Step 2 – Understanding the Data
Answering the following questions…
 What is the available data?
 Do we need to acquire other data? (Publicly available/ Buy data)
 What is the nature of the dataset? (Data profiling)
− Number of cases
− Number of attributes
− Missing values (sparsity)
− Numerical variables (min, max, mean, media, stdv. , outliers)
− Categorical variables (cardinality, frequencies, mode value)
− Correlations between numerical variables
− Statistical dependency between categorical variables.
− Statistical variance (numerical vs. categorical variables)
− Inconsistencies (based on business rules)
Should lead to…
 Identify the data pre-processing operation needed.
 Suggest the model to be used.
exploratory data analysis
CRISP-DM Process:
• Modelling
• Deployment
Data Profiling:
• Number of cases
• Number of attributes
• Missing values
• Numerical variables
− Min - Max - Median
− Distribution(Mean, stdv.)
− Outliers
• Categorical variables
− Cardinality
− Frequencies
• Correlations/Dependencies
• Inconsistencies

Step 3 – Preparing the Data
Feature Engineering: Building the dataset.
 Feature Construction: fabricating a set of (possibly) useful features.
Example - Input: Sales Transactions (Customer, Product, Orders)
- Objective: Customer Segmentation
- Features: Days First Purchase, Days Last Purchase, Avg. Days between 2
Purchase, Last 3 months total Spending, Last 6 Month Total Spending, Promotion
Responsiveness, New Product Responsiveness, Avg. Purchased Product Price, …, Web Usage
Information, Demographics, Geographic, Economic Indices, Date Indicators, etc.
 Feature Selection: Selecting the most effective subset of the available
features – Filter vs. Wrapper
 Feature Extraction: constructing a new set of independent (uncorrelated)
features, from the existing feature set, using mathematical transformation –
Principal Component Analysis (PCA), Factor Analysis (FA), etc.
good luck is a residue of preparation…
CRISP-DM Process:
• Modelling
• Deployment
Data Preparation:
• Feature Engineering
− Feature Construction
− Feature Selection
− Feature Extraction
• Type Conversion
− Discretisation
− To Numeric
• Variable Tuning
− Missing values
− Clipping
− Scaling
• Row Processing
− Aggregation
− Removing duplicates
− Sampling
− Data Reduction

Step 3 – Preparing the Data
Variable Type Conversion:
 Numerical to Categorical (Discretisation) → Equal Width/ Equal Size/ Supervised.
 Categorical to Numerical → Hot-one/ Relative Counts
Variable Tuning:
 Missing Values → Eliminate/ Estimate.
 Clipping Extreme Values → Fix/ Remove.
 Scaling → Normalisation/ Standardisation.
Row Processing:
 Aggregation
 Removing Duplicates
 Instance Selection (Data Reduction)
 Sampling/Partitioning
“garbage in–garbage out”…
CRISP-DM Process:
• Modelling
• Deployment
Data Preparation:
• Feature Engineering
− Feature Construction
− Feature Selection
− Feature Extraction
• Type Conversion
− Discretisation
− To Numeric
• Variable Tuning
− Missing values
− Clipping
− Scaling
• Row Processing
− Aggregation
− Removing duplicates
− Sampling
− Data Reduction

Step 4 - Modelling
If you interrogate the data, it will confess…
CRISP-DM Process:
• Modelling
• Deployment
Modelling Variation:
• Approaches
• Algorithms
• Parameters
• Dataset Representations
 Overall Procedure:
 sets = Split( dataset, ratio);
 train=sets[0]; test=sets[1];
 model=Build(algorithm, train, preproc, param);
 Visualize(model);
 quality= Evaluate( model, test, measure);
 Always Build Multiple Models:
 Using different approaches.
 Using different algorithms.
 Using different parameters (parameter sweeping).
 Using different dataset representations.
 Empirical Evaluation for Model Selection

Step 5 – Evaluation and Interpretation
Model Predictive Effectiveness
 Predictive Accuracy
Model Comprehensibility
 Interpretability → Insights
 Model acceptance
 Legal explanation (Justifiability)
− Credit Denial
− Medical Decisions
Algorithm Efficiency
 Scalability/running time
 User Input parameters
Performance Quality Aspects
CRISP-DM Process:
• Modelling
• Deployment
Predictive Model Quality
• Predictive Effectiveness
• Comprehensibility
• Scalability, running time
• User input parameters

Step 5 – Evaluation and Interpretation
Predictive Models – Predictive Effectiveness (accuracy?)
 Considerations
− Imbalance Class
− Misclassification Cost (Expected Utility)
− Single Class Focus (Hits Rate vs. False Alarms)
 Measures
− Confusion Matrix
− Accuracy (Micro vs. Macro)
− Precision, Recall, Sensitivity, Specificity, F-Measure, etc.
− Area Under Curve, lift Chart, Profit/Cost Chart, etc.
− QLF, BIR, etc. (Probabilistic Classification/Regression)
 Methods
− Hold-out
− k-fold Cross Validation
− Leave-one-out
Descriptive Models – It is up to you!
all models are wrong, but some are useful…
Actual
Predicted
Positive Negative
Positive TP FP
Negative FN TN
CRISP-DM Process:
• Modelling
• Deployment
Predictive Model Quality
• Predictive Effectiveness
• Comprehensibility
• Scalability, running time
• User input parameters
Predictive Quality Measures:
• Accuracy (Micro vs. Macro)
• Precision vs. Recall
• Sensitivity vs. Specificity
• Kappa – Lift – odds
• QLF, CE, BIR
• AUC, lift, cost charts
Evaluation Methods:
• Hold-out
• k-fold Cross Validation

Step 6 – Deployment
data mining in action!
CRISP-DM Process:
• Modelling
• Deployment
Demo
Tools & Technologies
• MS Azure ML
• MS Analysis Services
• Infer.NET
• WEKA (JAVA)
• R Statistics (caret, rattle)
• Python (Mlpy, scikit-learn)
• OpenML
• C/C++ - Matlab
• SAS
• SPSS
• RapidMiner
• Apache Mahout
Dataset Repository
• UCI - KDD
• data.gov.uk
• GapMinder

Screenshot – Decision Trees
Microsoft Analysis Services

Screenshot – Cluster Analysis

Screenshot – Association Rules Analysis

Screenshot – Time Series

Screenshot – ML Experiment
Microsoft Azure Machine Learning

Screenshot – ML Web Services
Microsoft Azure Machine Learning

Screenshot – Probabilistic Models
Microsoft Infer.net

Screenshot – Classification Rules
Java - WEKA

Screenshot – Text Mining
R Statistics

Screenshot – Regression Models
R Statistics

Concluding Remarks
a few takeaways…
• Understand the business problem first, please!
• Use the appropriate tool/technique that best suits the business problem, not the other way around.
• Start by solving simple business problems first, before moving to complex ones (BI Insight Maturity Journey).
• Spend sometime to explore and understand the data.
• Incorporate domain knowledge in your analysis (avoid reinventing the wheel!).
• Data preparation is very important for building effective models.
• Data mining is an experimental/ iterative process (not ideal for fixed-price projects!).
• Try to tackle the business problem with different analytic approaches.
• It is clever to solve complex problems with simple techniques.

My Background
Applying Ant Colony Optimisation (ACO) in Building Classification Models
• Honorary Research Fellow, School of Computing , University of Kent.
• Ph.D. Computer Science, University of Kent, Canterbury, UK.
• M.Sc. Computer Science , The American University in Cairo, Egypt.
• 20+ published journal and conference papers, focusing on:
– classification rules induction,
– decision trees construction,
– Bayesian classification modelling,
– data reduction,
– instance-based learning, and
– evolving neural networks.
• Journals: Swarm Intelligence, Swarm & Evolutionary Computation,
Intelligent Data Analysis, Applied Soft Computing, and Memetic Computing.
• Conferences: ANTS, IEEE CEC, IEEE SIS, EvoBio, ECTA, and INNS-BigData.

Data Mining - The Big Picture!

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