What is Data Mining?
Before understanding, Data Mining Concepts and Techniques first we will study data mining. Data mining is a feature of the conversion of data into some knowledgeable information. This refers to the process of getting some new information by looking into a large amount of data available. Using various techniques and tools, one can predict the information that is required from the data, only if the procedure followed is correct. This is helpful in various industries to extract some required information for future analysis by recognizing some patterns in the existing data in databases, data warehouses, etc.
Types of Data in Data Mining
Following are the types of data on which data mining can be performed:
- 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
Below are the points for Data mining process:
1. Business Understanding
This is the first phase of the data mining implementation process where all the needs and the client’s objective of business are clearly understood. Proper data mining goals are set keeping in view the current scenario in the business and other factors such as resources, assumptions, constraints. A proper data mining plan should be in detail and must fulfill our business and data mining goals.
2. Data Understanding
This phase act as a sanity check on the data that has been collected from various resources for data mining processes.first all the data from the different sources are collected related to the organization business scenario which can be in the various database, flat files, etc. The collected data is checked that they are matching properly as they can be unrelatable.
Sometimes metadata also needs to be checked to reduce the errors in the data mining processes. Various data mining queries are used for the analysis of correct data and based on the results data quality can be checked. It also helps to analyze if any data is missing or not.
3. Data Preparation
This process consumes the maximum time of the project. This face includes a process called data cleaning to clean the data that has been collected during the data understanding process. The data cleaning process is used to clean the data to exclude improper noisy data for the data with missing values.
4. Data Transformation
In the next state data transformation operations are performed 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 to make the data ready for the data modeling process.
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This is the phase in data mining where the proper technique is used to determine the patterns of data. The various scenario has to be created to check the quality and validity of this model and to determine whether the goals that have been defined in the business understanding process are being met after implementation of those techniques. The pattern that has been found in this process is further evaluated and is sent for the deployment to the business operations team so that it can help to improve organizations’ business policy.
In this phase, the proper evaluation of the data mining discoveries is made to give it a go or no go for the implementation in the business processes. A proper comparison is made with the discoveries and the existing business operations plan to properly evaluate the change for the information found needs to be added to the current business operations.
In this phase, the information that has been concluded using data mining processes are transformed train understandable form for non-technical stakeholders. For this process, a proper deployment plan that includes shipping, maintenance and monitoring of the information found is created. In this way, proper project report is created along with the experiences and the lessons learned during the process to handover our data mining discoveries to the business operations team.
Hence this process helps to improve the business policy of an organization.
Data Mining Techniques
Below techniques and technologies can help to apply data mining feature in its most efficient manner:
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 of some aberration. For example, it can be seen if a particular person travels around different countries then that person will require to book tickets on a regular basis, thus a special credit card can be offered.
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. For example, while analyzing the data for traffic in the city, the area’s traffic can be classified under low, medium and heavy. This will help the travelers to predict the traffic before time.
This technique is similar to the pattern tracking technique but here it is related to the dependently linked variables. That means the pattern for the related data is found that is linked to the existing data. Event-related to the other event is tracked and the particular patterns are found in that data. For example, file tracking data for the traffic in a particular City one can also track, the most visited places in a city. This can also help to track famous places to be visited in the City.
4. Outlier Detection
This technique is related to the extraction of anomalies in the pattern of data. For example, the sale of a mall makes a good profit over the 11 months of the year but in the last month, the sales get dropped that much that it leads to facing loss. In these cases, we need to find out what was the factor that made the reduction in the sales so that one can avoid it next time. The technique of finding such a distraction in the regular pattern is part of the Outlier detection technique.
This technique is similar to classification, only the difference lies that it picks the group of data that have some similarities put them in a single group. For example, clustering different audiences of a cinema on the basis of frequency that how often they come for shows, which timing they come for most often and which genre of movie they come for.
This technique helps to draw the relationship between the 2 variables on which an analysis could depend upon. Here we try to find out the pattern of change in the variable by fixing the other dependent variables. For example, if we need to find out the pattern in sales of a product in a mall depending on its availability, season, demand, etc. This can lead the owner to fix the price for selling it.
The most important feature of data mining is to reduce future risks and increase the profit of the organization by studying the existing and history patterns for sales and credit risks. Here this type of technology helps us to take future decisions depending on the pattern found in historical and present data and keeping market change and risks in mind. This technique is most helpful for data mining.
Data Mining Tools
One doesn’t need the particular latest technologies for performing data mining. It can be done using the latest database systems also, and simple tools that are easily available in any organization. Also one can create its own tool when the appropriate tool is missing. The most popular tool is widely used in the industry are given below:
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:
- Classical statistical tests
- Time-series analysis
- 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 further to 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 states.
- Difficulties faced in managing large databases.
- May be need arises 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-Data Mining Concepts and Techniques
- Data mining is a way for tracking the past data and make future analysis 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, Deployment.
- Various data mining techniques are available to make data mining work in an efficient manner such as classification, regression association, etc. Usage depends on the scenario.
- The most effective data mining tools are R-language and Oracle Data.
- The main disadvantage of data mining that is faced is the difficulties in training experts to operate that analytics software.
- There are diverse industries that use data mining for their analysis purpose such as banking, manufacturing, supermarkets, retail service providers, etc.
This is a guide to Data Mining Concepts and Techniques. Here we discuss the Data Mining process, techniques, and tools in Data Mining. You can also go through our other related articles to learn more-