Overview of the Data Mining Process
The data mining process is used to get the pattern and probabilities from the large dataset due to which it is highly used in business for forecasting the trends, along with this it is also used in fields like Market, Manufacturing, Finance, and Government to make predictions and analysis using the tools and techniques like R-language and Oracle data mining, which involves the flow of six different steps.
One of the essential tasks of data mining relates to the automatic and semi-automatic analysis of large quantities of raw data and information to extract the previously unknown exciting set of patterns such as clusters or a group of data records, anomaly detection (unusual forms) and also in the case of dependencies which makes use of sequential pattern mining and association rule mining. This makes use of spatial indices. These patterns can be known to be among the kinds in the input data and can be used in further analysis, such as predictive analysis and machine learning. More accurate sets of results can be obtained once you start making use of support decision systems.
How does Data Mining Work?
There is an abundance of data in the industry across domains, and it becomes essential to treat and process the data accordingly. Basically, in a nutshell, it involves the ETL set of processes such as the extraction, transformation, and loading of the data and everything else required for this ETL to happen. This involves the cleansing, change, and processing of data in various systems and representations. The clients can use this processed data to analyse the businesses and the trends of growth in their companies.
Advantages
The advantage of data mining includes the ones related to business and ones like medicine, weather forecast, healthcare, transportation, insurance, government, etc. Some of the advantages include:
- Marketing/Retail: It helps all the marketing companies and firms to build models which are based on a historical set of data and information to predict the responsiveness to the marketing campaigns prevailing today such as online marketing campaign, direct mail, etc.
- Finance/Banking: The data mining involves financial institutions provide information about loans and also credit reporting. When the model is built on historical information, good or bad loans can then be determined by the financial institutions. Furthermore, fraudulent and suspicious transactions are monitored by the banks.
- Manufacturing: The faulty equipment and the quality of the manufactured products can be determined by using the optimal parameters for controlling. For example, for some of the semi-conductor development industries, water hardness and quality become a major challenge as they tend to affect the quality of their product’s production.
- Government: The governments can be benefitted from the monitoring and gauging the suspicious activities to avoid anti-money laundering activities.
Different Stages of Data Mining Process
The different stages of the data mining process are as follows.
- Data cleansing: This is the initial stage in data mining, where the classification of the data becomes an essential component to obtain final data analysis. It involves identifying and removing inaccurate and tricky data from a set of tables, databases, and record sets. Some techniques include the ignorance of tuple, which is mainly found when the class label is not in place; the next approach requires filling the missing values on its own, replacing missing values and incorrect values with global constants or predictable or mean values.
- Data integration: It is a technique that involves merging the new set of information with the existing group. The source may, however, involve many data sets, databases or flat files. The customary implementation for data integration is creating an EDW (enterprise data warehouse), which then talks about two concepts- tight and loose coupling, but let’s not dig into the detail.
- Data transformation: This requires transforming data within formats, generally from the source system to the required destination system. Some strategies include Smoothing, Aggregation, Normalization, Generalization, and attribute construction.
- Data discretization: The technique that can split the continuous attribute domain along intervals is called data discretization. The datasets are stored in small chunks, thereby making our study much more efficient. Two strategies involve Top-down discretization and bottom-up discretization.
- Concept hierarchies: They minimize the data by replacing and collecting low-level concepts from high-level concepts. Concept hierarchies define the multi-dimensional data with multiple levels of abstraction. The methods are Binning, histogram analysis, cluster analysis, etc.
- Pattern evaluation and data presentation: If the data is presented efficiently, the client and the customers, can make use of it in the best possible way. After going through the above set of stages, the data is presented in graphs and diagrams and thereby understanding it with minimum statistical knowledge.
Tools and Techniques
Data mining tools and techniques involve how these data can be mined and be put to fair and effective use. The following two are among the most popular set of tools and techniques of data mining:
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1. R-language: It is an open-source tool that is used for graphics and statistical computing. It has a wide variety of classical statistical tests, classification, graphical techniques, time-series analysis, etc. It makes use of effective storage facilities and data handling.
2. Oracle data mining: It is popularly known as ODM, which becomes a part of Oracle advanced analytics database, thereby generating detailed insights and predictions specifically used to detect customer behaviour, develop customer profiles, and identify cross-selling ways opportunities.
Conclusion
Data mining is all about explaining historical data and a real streaming set of data, thereby making use of predictions and analysis on top of the mined data. It is closely related to data science and machine learning algorithms such as classification, regression, clustering, XGboosting, etc., as they tend to form essential data mining techniques.
One of the drawbacks can include the training of resources on software, which can be a complicated and time-consuming task. Data mining becomes a necessary component of one’s system today, and by making efficient use of it, businesses can grow and predict their future sales and revenue. I hope you liked this article. Stay with us for more like these.
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