Learning Method In Data Mining

Mar 5, 2018Download as pptx, pdf0 likes1,343 views

ishaq zaman

Nice and in simple english easy to present breifly described every topics with there algorithms and examples.

WHAT IS LEARNING?
• Process of learning begins with observation of data such as, examples,
direct experience or instructions.
• Its aim is to allow the computers learn automatically without human
intervention or assistance and adjust actions accordingly.

LEARNING METHODS ARE..
• Supervised Machine learning algorithms
• Unsupervised Machine learning algorithms
• Semi-supervised Machine learning algorithms
• Reinforcement Machine learning algorithms

SUPERVISED MACHINE LEARNING
• Can apply what has been learned in the past to new data using labeled
examples to predict future events.
• Starting from the analysis of a known training dataset, the learning
algorithm produces an inferred function to make predictions about the
output values.
• Algorithm analyzes the training data set and produces an inferred
function.
• If the output of the function is discrete than it is called classifier and if
the output is continuous than it is called a regression function.

EXAMPLE
• If the inputs are 1,2,3,4,5,6 and the outputs according to the inputs are
1,4,9,16,25,36
• Then we can predict the next output by the help of function which we
get from above which is output=input^2
• So if the next input is 7 than by putting in function the output will be 49

SUPERVISED LEARNING ALGORITHM
• Support Vector Machine Algorithm (SVM)

UNSUPERVISED MACHINE LEARNING
• No labels are given to the learning algorithm, leaving it on its own to
find structure in its inputs
• Unsupervised learning can be a goal in itself (discovering hidden
patterns in data).
• The data have no target attribute.

EXAMPLE
• You have bunch of photos of 6 people but without information who is
on which one and want to divide this dataset into 6 piles, each with
photos of one individual.

UNSUPERVISED LEARNING
ALGORITHM
• K – means clustering

SEMI-SUPERVISED LEARNING
• Semi-supervised learning falls in between Supervised and Unsupervised.
• Semi-supervised learning use small amount of labeled data and large
amount of unlabeled data.
• The goal is to learn a predictor that predicts future test data better than
the predictor learned from the labeled training data alone.
• This for example can be used in Deep belief networks, where some
layers are learning the structure of the data (unsupervised) and one
layer is used to make the classification (trained with supervised data)

REINFORCEMENT MACHINE LEARNING
ALGORITHMS
• Is a learning method that interacts with its environment by producing actions and
discovers errors or rewards.
• Reinforcement learning algorithm (called the agent) continuously learns from the
environment in an iterative fashion. In the process, the agent learns from its
experiences of the environment until it explores the full range of possible states.
• This method allows machines and software agents to automatically determine the
behavior within a specific context in order to maximize its performance

STEPS REINFORCEMENT ALGO WORK..
• In order to produce intelligent programs (also called agents), reinforcement learning
goes through the following steps:
• Input state is observed by the agent.
• Decision making function is used to make the agent perform an action.
• After the action is performed, the agent receives reward or reinforcement from the
environment.
• The state-action pair information about the reward is stored.

REINFORCEMENT ALGO..
• Q-learning
• Q(state, action) = R(state, action) + Gamma * Max[Q(next state, all actions)]

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Controlling Financial Processes at a MunicipalityProcess mining Evangelist

The fourth speaker at Process Mining Camp 2018 was Wim Kouwenhoven from the City of Amsterdam. Amsterdam is well-known as the capital of the Netherlands and the City of Amsterdam is the municipality defining and governing local policies. Wim is a program manager responsible for improving and controlling the financial function. A new way of doing things requires a different approach. While introducing process mining they used a five-step approach: Step 1: Awareness Introducing process mining is a little bit different in every organization. You need to fit something new to the context, or even create the context. At the City of Amsterdam, the key stakeholders in the financial and process improvement department were invited to join a workshop to learn what process mining is and to discuss what it could do for Amsterdam. Step 2: Learn As Wim put it, at the City of Amsterdam they are very good at thinking about something and creating plans, thinking about it a bit more, and then redesigning the plan and talking about it a bit more. So, they deliberately created a very small plan to quickly start experimenting with process mining in small pilot. The scope of the initial project was to analyze the Purchase-to-Pay process for one department covering four teams. As a result, they were able show that they were able to answer five key questions and got appetite for more. Step 3: Plan During the learning phase they only planned for the goals and approach of the pilot, without carving the objectives for the whole organization in stone. As the appetite was growing, more stakeholders were involved to plan for a broader adoption of process mining. While there was interest in process mining in the broader organization, they decided to keep focusing on making process mining a success in their financial department. Step 4: Act After the planning they started to strengthen the commitment. The director for the financial department took ownership and created time and support for the employees, team leaders, managers and directors. They started to develop the process mining capability by organizing training sessions for the teams and internal audit. After the training, they applied process mining in practice by deepening their analysis of the pilot by looking at e-invoicing, deleted invoices, analyzing the process by supplier, looking at new opportunities for audit, etc. As a result, the lead time for invoices was decreased by 8 days by preventing rework and by making the approval process more efficient. Even more important, they could further strengthen the commitment by convincing the stakeholders of the value. Step 5: Act again After convincing the stakeholders of the value you need to consolidate the success by acting again. Therefore, a team of process mining analysts was created to be able to meet the demand and sustain the success. Furthermore, new experiments were started to see how process mining could be used in three audits in 2018.

Dr. Robert Krug - Expert In Artificial IntelligenceDr. Robert Krug

Dr. Robert Krug is a New York-based expert in artificial intelligence, with a Ph.D. in Computer Science from Columbia University. He serves as Chief Data Scientist at DataInnovate Solutions, where his work focuses on applying machine learning models to improve business performance and strengthen cybersecurity measures. With over 15 years of experience, Robert has a track record of delivering impactful results. Away from his professional endeavors, Robert enjoys the strategic thinking of chess and urban photography.

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Learning Method In Data Mining

1. LEARNING METHODSG R O U P M E M B E R S M . I S H A Q Z A M A N A R S L A N N A Z I R B I L A L L AT I F

2. WHAT IS LEARNING? • Process of learning begins with observation of data such as, examples, direct experience or instructions. • Its aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.

3. LEARNING METHODS ARE.. • Supervised Machine learning algorithms • Unsupervised Machine learning algorithms • Semi-supervised Machine learning algorithms • Reinforcement Machine learning algorithms

4. SUPERVISED MACHINE LEARNING • Can apply what has been learned in the past to new data using labeled examples to predict future events. • Starting from the analysis of a known training dataset, the learning algorithm produces an inferred function to make predictions about the output values. • Algorithm analyzes the training data set and produces an inferred function. • If the output of the function is discrete than it is called classifier and if the output is continuous than it is called a regression function.

5. EXAMPLE • If the inputs are 1,2,3,4,5,6 and the outputs according to the inputs are 1,4,9,16,25,36 • Then we can predict the next output by the help of function which we get from above which is output=input^2 • So if the next input is 7 than by putting in function the output will be 49

6. SUPERVISED LEARNING ALGORITHM • Support Vector Machine Algorithm (SVM)

7. UNSUPERVISED MACHINE LEARNING • No labels are given to the learning algorithm, leaving it on its own to find structure in its inputs • Unsupervised learning can be a goal in itself (discovering hidden patterns in data). • The data have no target attribute.

8. EXAMPLE • You have bunch of photos of 6 people but without information who is on which one and want to divide this dataset into 6 piles, each with photos of one individual.

9. UNSUPERVISED LEARNING ALGORITHM • K – means clustering

10. SEMI-SUPERVISED LEARNING • Semi-supervised learning falls in between Supervised and Unsupervised. • Semi-supervised learning use small amount of labeled data and large amount of unlabeled data. • The goal is to learn a predictor that predicts future test data better than the predictor learned from the labeled training data alone. • This for example can be used in Deep belief networks, where some layers are learning the structure of the data (unsupervised) and one layer is used to make the classification (trained with supervised data)

11. REINFORCEMENT MACHINE LEARNING ALGORITHMS • Is a learning method that interacts with its environment by producing actions and discovers errors or rewards. • Reinforcement learning algorithm (called the agent) continuously learns from the environment in an iterative fashion. In the process, the agent learns from its experiences of the environment until it explores the full range of possible states. • This method allows machines and software agents to automatically determine the behavior within a specific context in order to maximize its performance

12. STEPS REINFORCEMENT ALGO WORK.. • In order to produce intelligent programs (also called agents), reinforcement learning goes through the following steps: • Input state is observed by the agent. • Decision making function is used to make the agent perform an action. • After the action is performed, the agent receives reward or reinforcement from the environment. • The state-action pair information about the reward is stored.

13. ARCHITECTURE

14. REINFORCEMENT ALGO.. • Q-learning • Q(state, action) = R(state, action) + Gamma * Max[Q(next state, all actions)]

Learning Method In Data Mining

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