Special Concepts and Data Mining Techniques

Introduction.

In today’s article, we will talk about the concept and techniques of data mining. Data mining is a feature of converting data into well-known information. This refers to getting new information by looking at inflation data in our time. We make use of different techniques and tools, and one can predict the required information from the data only if the procedure followed is correct. This helps different industries to extract some information required for future analysis by recognizing some patterns in the data in databases, data warehouses, etc.

What is data mining?

Data mining is used to build machine learning models including search engine technology and website recommendation programs. It is the procedure of capturing large sets of data in order to identify the insights and visions of the data.

Types of Data in Data Mining

we have more types of data mining :

• Relational databases
• Data warehouses
• Advanced DB and information repositories
• Object-oriented and object-relational databases
• Transactional and Spatial databases
• Heterogeneous and legacy databases
• Multimedia and streaming database
• Text databases
• Text mining and Web mining

Data Mining Process

1. Business Understanding.
2. Data Understanding.
3. Data Preparation.
4. Data Transformation.
5. Modeling.
6. Deployment.

Can we explain those steps step by step?

1. Business understanding

This is the first stage of the data mining implementation process, where all the needs and business objectives of the client are clearly understood. Appropriate data mining objectives are set, considering how the company’s current subject matter will run and other factors such as resources, assumptions, and limitations. An appropriate data mining plan should be detailed and should meet our business and data mining objectives.

2. Understanding the data

In this stage, the integrity of the data collected from different sources is checked for data mining operations. First, all the data from different sources are organized in relation to the business scenario of the enterprise, which can be in various databases, static files, etc.

3. Data processing

This is the third stage. This process consumes the maximum project time. This aspect includes a process called data cleaning to clean up the data collected during the data comprehension process. Data cleaning is used to clean the data to exclude cluttered data that is not suitable for data with missing values.

4. Data conversion

This stage is the fourth stage where data transformation operations are performed in the following case, which is used to change the data to make it useful for the data mining implementation process. Here transformation such as aggregation, generalizations, normalization, or attribute construction prepares the data for the modeling process.

5. Modeling

This stage is considered to be the stage in which data is extracted, where appropriate technology is used to identify data patterns. Various scenarios should be created to check the quality and viability of this model and to determine whether the objectives set in the business understanding process have been met after implementing those techniques. The pattern found in this process is further evaluated and sent for publication to the Business Operations team so that it can help improve the organization’s business policy.

6. Evaluation

In this phase, the proper evaluation of the data mining discoveries is made to give it a go or no go to implement the business processes. A fair comparison is made with the discoveries. The existing business operations plan to evaluate the change for the information properly found needs to be added to the current business operations.

Data Mining Techniques

1. Track the Patterns

Recognizing the patterns in your dataset is one of the basic techniques in data mining. The data is observed at regular intervals for recognizing some aberrations.

2. Classification

It is one of the complex techniques for data mining, where we need to make various discernable categories using various attributes in the existing data. These categories help to reach various conclusions for our future use.

3. Association

This technique is similar to the pattern tracking technique, but it is related to the dependently linked variables. That means the pattern for the corresponding data is found that is linked to the existing data. Event-related to the other event are tracked, and the particular ways are found in that data.

4. Outlier Detection

This technique is related to the extraction of anomalies in the pattern of data.

5. Clustering

This technique is similar to classification; only the difference lies in that it picks the group of data that have some similarities and put them in a single group.

6. Regression

This technique helps to draw the relationship between the 2 variables upon which an analysis could depend. Here we try to determine the pattern of change in the variable by fixing the other dependent variables.

7. Prediction

The most important feature of data mining is reducing future risks and increasing the organization’s profit by studying the existing and historical patterns for sales and credit risks. Here this type of technology helps us make future decisions depending on the way found in historical and present data and keeping market change and threats in mind. This technique is most helpful for data mining.

Data Mining Tools:

1. R-Language

This is an open-source tool that is used for statistical computing and graphics. This tool helps in effective data handling and storage facility ad these all features are because of the below techniques:

Statistical

• Classical statistical tests
• Time-series analysis
• Classification
• Graphical Techniques

2. Oracle Data Mining

This tool is popularly known as ODM; it is a part of the Oracle Advanced Analytics Database. This tool helps to analyze data in data warehouses and generates detailed insights that help make predictions. These things help to study customer behavior; products demand ad thus help in increments of selling opportunities.

Challenges being faced in the implementation of Data mine:

Skilled experts are needed to make complex data mining queries. Present models may not fit in the future state’s databases. may not fit future conditions.

Difficulties faced in managing large databases.

It may be necessary to modify business practices to use information that has been uncovered. Heterogeneous databases and information coming globally can result in complex integrated information. Data mining has a prerequisite that data must be diverse in nature. Otherwise, results can be inaccurate.

Conclusion

In this article data mining concepts and techniques, Data mining is a way of tracking past data and making future analyses using it. It is the same as extracting the information required for analysis from last-date assets that are already present in the databases. Data mining can be done on various types of databases like spatial data basis, RDBMS, data warehouses, multiple and legacy databases, etc. The Whole mining process includes business understanding, Data Understanding, Data Preparation, Modelling, Evolution, and Deployment.

Mohamed B Mahmoud. Data Scientist.