5. Definition
Deploying the Data Mining Model
Evaluation Phase
Building Data Mining Model
Preperation for Creating a Data Mining Model
Exploring Database
Creating Database
Problem Definition
11. Area of Usage
Financial Data Analysis
Retail Industry
Telecommunication Industry
Biological Data Analysis
Other Scientific Applications
Intrusion Detection
Good morning,
I am Yaman Çakmaklar. I am going to present you the subject attracts my interest. So this is datamining the most exotic branch of computer science. John Naisbitt the professor and the writer said «We are drowning in data but starving for knowledge».
Here is the content. I am going to give a brief speech about all content. First, I will start with definition then why and when we need to use datamining. Other steps will be the concept of the case.
Data is the significant part of the mining. So what is data? Data is unprocessed facts and figures without any added interpretation or analysis. Information is data that has been interpreted so that is has meaning for the user.
Knowledge is combination of information, experince and insight that may benefit the individual or the organization. For example, « An access point is $100» this is data. But «The price of the Access point has risen from $90 to $100» this is information. So knowledge is like «if dollar rises %10, the Access point will be $100»
After the definitions, BI is another part of mining. First I will tell you the technical definition. BI is a technology-driven process for analysing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions. Basically it is used for gaining competetive advantage, consolidating the unprocessed data to get a business movement, decision making.
As regards to datamining. You can see the definition step by step on the slide like how to be mine. But technically it is extraction of interesting, non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases include tremendous amount of data.
So the order of datamining decisions. Starting with databases to be mined. . .
The steps are the path of how to mine. We will see the KDD( Knowledge Discovery in Databases)
on the next slide.
Knowledge discovery in databases process is on the schema. First, we clean the data in database. Then select the data from data warehouse. Task-Relevant Data occurs. Last step of process is datamining. It makes us evaluate the patterns to reach the knowledge. We can also go back step by step and start from previous phases.
The techniques are statistical algorithms. They are used for pattern evaluation.
A data warehouse is a subject-oriented, integrated, time- variant and non-volatile collection of data in support of managements’ decision-making process.or strategy.
Data mining is used for almost all sectors to improve decision-making. I will give you a basic example. In U.S. According to processed or mined data, baby diapers and beers are on the same section in a supermarket. It has seen that, man goes for shopping and they buy beer and diaper at the same time. Data mining is used for also increasing sales. So they unite the sections.
Finally, I wanted to adapt it to our job. Beginning with market and product research.
