School of Computer Science & Engineering Artificial Intelligence Machine Learning, Pattern Recognition, Data Mining Dae-Won Kim School of Computer Science & Engineering Chung-Ang University

AI Scope 3. Machine Learning/ Pattern Recognition/ Data Mining 1. Search-based optimization techniques for real-life problems Hill climbing, Branch and bound, A*, Greedy algorithm Simulated annealing, Tabu search, Genetic algorithm 2. Reasoning: Logic, Inference, and knowledge representation Logical language: Syntax and Semantics Inference algorithm: Forward/Backward chaining, Resolution, and Expert System 3. Machine Learning/ Pattern Recognition/ Data Mining Classification: Bayesian algorithm, Nearest-neighbor algorithm, Neural network Clustering: Hierarchical algorithm, K-Means algorithm 4. Uncertainty based on Probability theory 5. Planning, Scheduling, Robotics, and Industry Automation

Have you ever heard of Big Data?

Progress in digital data acquisition and storage technology has resulted in the growth of huge databases.

Data mining is the extraction of implicit, previously unknown, and potentially useful information from data.

We build algorithms that sift through databases automatically, seeking patterns.

Strong patterns, if found, will likely generalize to make accurate predictions on future data.

Algorithms need to be robust enough to cope with imperfect data and to extract patterns that are inexact useful.

Machine learning provides the technical basis of data mining.

We will study simple machine learning methods, looking for patterns in data.

People has been seeking patterns in data since human life began. e.g., Samsung Galaxy: Samsung Pay, Managers in Samsung want to find consuming patterns of users so that it’d provide personalized services.

In data mining, computer algorithm is solving problems by analyzing data in databases.

Data mining is defined as the process of (knowledge) discovering patterns in data.

Data mining is defined as the process of (knowledge) discovering patterns in data.

We start with a simple example.

Q: Tell me the name of this fish.

Algorithm ??

We have 100 fishes, and measured their lengths. (e. g We have 100 fishes, and measured their lengths. (e.g., fish: x=[length]t)

Our algorithm can measure the length of a new fish, and estimate its label.

Yes, it is a typical prediction task through classification technique Yes, it is a typical prediction task through classification technique. But, it is often inexact and unsatisfactory.

Next, we measured their lightness. (e.g., fish: x=[lightness])

Lightness is better than length.

Let us use both lightness and width. (e. g Let us use both lightness and width. (e.g., fish: x=[lightness, width])

Each fish is represented a point (vector) in 2D x-y coordinate space.

Everything is represented as N-dimensional vector in coordinate space.

The world is represented as matrix

We assume that you have learned the basic concepts of linear algebra.

The objective is to find a line that effectively separates two groups.

How to find the line using a simple Math from high school?

We can build a complex nonlinear line to provide exact separation.

The formal procedure is given as:

This shows a predictive task of data mining, often called as pattern classification/ recognition/ prediction.

The act of taking in raw data and making an action based on the category of the pattern.

We build a machine that can recognize or predict patterns.

Q: How to represent and classify texts? Opinion mining Sentiment analysis

Another famous task of data mining is a descriptive task Another famous task of data mining is a descriptive task. Cluster analysis is the well-known group discovery algorithm.

We will experience the basic issues in the prediction task (pattern classification) in forthcoming weeks.

Some terms should be defined.

Given training data set : ‘n x d’ pattern/data matrix: Fish Lightness Length Weight Width Class Label Fish-1 10 70.3 6.0 36 Salmon Fish-2 75.5 8.8 128 Fish-3 29 51.1 9.4 164 Sea bass Fish-4 49.9 8.4 113

Given training data set : ‘n x d’ pattern/data matrix: ‘d’ features (attributes, variables, dimensions, fields) Fish Lightness Length Weight Width Class Label Fish-1 10 70.3 6.0 36 Salmon Fish-2 75.5 8.8 128 Fish-3 29 51.1 9.4 164 Sea bass Fish-4 49.9 8.4 113 ‘n’ patterns (objects, observations, vectors, records)

Classification General description Specific terms Supervised pattern classification Labeled training patterns, the groups are known a priori Constructs rules for classifying new data into the known groups Specific terms Pattern=object=observation is represented as a feature vector Distance measure for numeric and categorical data Training set (answer database) and test set (new observation) Prediction performance by accuracy, sensitivity, specificity, … ex) Bayesian classifier, Nearest-neighbor classifier, SVM, NN, LDA, …

Each pattern is represented as a feature vector.

The training pattern matrix is stored in a file or database.

Given labeled training patterns, the class groups are known a priori.

We constructs algorithms to classify new data into the known groups.

Training data vs. Test data

Training data are used as answers Training data are used as answers. We are learning algorithms using training data.

Test data are a set of new unseen data Test data are a set of new unseen data. We predict class labels using the learned algorithm.

Training data Test data # of data # of features data index feature-1 … feature-N class label Feature-1 Test data # of data # of features data index feature-1 feature-2 … feature-N Feature-1

For example, we try to classify the tumor type of breast cancer patients

Breast-cancer-training.txt 100 30 Patient-1 165 52 … 210 cancer Patient-2 170 50 230 normal Patient-100 160 47 250 Breast-cancer-test.txt 10 163 55 215 155 240 Patient-10 45 235

To evaluate the performance of prediction algorithms, we need a performance measure (Accuracy).

Accuracy = (True Positive + True Negative) / Gold Standard (Truth) Positive Negative Prediction Result True Positive False Positive False Negative True Negative Suspicious Patients with Breast Cancer Positive (Cancer) Negative (Normal) Accuracy = (True Positive + True Negative) / (True Positive + False Positive + False Negative + True Negative)

Accuracy = (30 + 55) / (30 + 5 + 10 + 55) = 0.85 (85%) Gold Standard (Truth) Positive Negative Prediction Result True Positive False Positive False Negative True Negative Suspicious Patients with Breast Cancer Positive (Cancer) Negative (Normal) 30 5 10 55 Accuracy = (30 + 55) / (30 + 5 + 10 + 55) = 0.85 (85%)

References # Textbooks # Advanced Topic: Emotional data mining 1) R.O. Duda, et al., “Pattern Classification” 2) S. Theodoridis., “Pattern Recognition” 3) T.M. Mitchell., “Machine Learning” # Advanced Topic: Emotional data mining Personalized music recommendation (Nov.)