Overview of the Data Mining Process
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 very essential tasks of data mining relates to the automatic and semi-automatic analysis of large quantities of raw data and information in order to extract the previously unknown very interesting set of patterns such as clusters or a group of data records, anomaly detection (unusual records) 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, for instance, in case of 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 very necessary to treat and process the data accordingly. Basically, in a nutshell, it involves the ETL set of processes such as the extraction, transformation and the loading of the data along with everything else which is required for this ETL to happen. This involves the cleansing, transformation, and processing of data to be used in various systems and representations. The clients can make use of this processed data for analyzing the businesses and the trends of growth in their companies.
Advantages
The advantage of data mining includes not only the ones related to business but also 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 historic set of data and information in order 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. Also, fraudulent and suspicious transactions are also monitored by the banks.
- Manufacturing: The faulty equipment and the quality of the manufactured products can be determined by making use of the optimal parameters for controlling. For example, for some of the semi-conductor development industries, water hardness and quality become a major challenge as it tends to affect the quality of produce of their product.
- Government: The governments can be benefitted with the monitoring and gauging the suspicious activities to avoid anti-money laundering activities.
Different Stages of Data Mining Process
The different stages of data mining process are as follows.
- Data cleansing: This is a very initial stage in the case of data mining where the classification of the data becomes an essential component to obtain final data analysis. It involves identifying and removal of inaccurate and tricky data from a set of tables, database, and recordset. Some techniques include the ignorance of tuple which is mainly found when the class label is not in place, the next technique requires filling of the missing values on its own, replacement of missing values and incorrect values with global constants or predictable or mean values.
- Data integration: It is a technique which involves the merging of the new set of information with the existing set. The source may, however, involve many data sets, databases or flat files. The customary implementation for data integration is the creation of an EDW (enterprise data warehouse) which then talks about two concepts- tight as well as loose coupling, but let’s not dig into the detail.
- Data transformation: This requires the transformation of 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 techniques which can split the domain of continuous attribute along intervals is called data discretization wherein the datasets are stored in small chunks and 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. The multi-dimensional data with multiple levels of abstraction are defined by concept hierarchies. The methods are Binning, histogram analysis, cluster analysis, etc.
- Pattern evaluation and data presentation: If the data is presented in an efficient manner, the client, as well as the customers, can make use of it in the best possible way. After going through the above set of stages the data then is presented in forms of graphs and diagrams and thereby understanding it with minimum statistical knowledge.
Tools and Techniques
Data mining tools and techniques involve the ways these data can be mined and be put to good and effective use. 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 facility 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 for the detection of customer behavior, develop customer profiles along with identification of cross-selling ways and opportunities.
Conclusion
Data mining is all about the explanation of historic data and also a real streaming set of data and thereby makes 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 important data mining techniques.
One of the drawbacks can include the training of resources on the set of software which can be a complex and time taking 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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