What Is Data Mining?
In this article we will be considering “Data Mining Process Model”. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
Data mining is the process of sorting through large data sets to identify patterns and relationships that can help solve business problems through data analysis. Data mining techniques and tools enable enterprises to predict future trends and make more-informed business decisions.
Data mining is a term related to “Data Warehousing of which I will elaborate on below;
What Is A Data Warehouse?
In computing, a data warehouse (also known as an enterprise data warehouse) is a system used for reporting and analyzing data and is considered a core component of business intelligence.
What Is Data Warehousing?
Data warehousing is the secure electronic storage of information by a business or other organization. The goal of a data warehouse is to create a trove of historical data that can be retrieved and analyzed to provide useful insight into the organization’s operations.
Related: What Does Data Warehousing Allow Organizations To Achieve
Importance Of Data Mining
- It helps companies gather reliable information
- It’s an efficient, cost-effective solution compared to other data applications
- It helps businesses make profitable production and operational adjustments
- Data mining uses both new and legacy systems
- It helps businesses make informed decisions
- It helps detect credit risks and fraud
- It helps data scientists easily analyze enormous amounts of data quickly
- Data scientists can use the information to detect fraud, build risk models, and improve product safety
- It helps data scientists quickly initiate automated predictions of behaviors and trends and discover hidden patterns
Types Of Data Mining
- Predictive Data Mining
- Descriptive Data Mining
- Classification Analysis
- Regression Analysis
- Time Serious Analysis
- Prediction Analysis
- Clustering Analysis
- Summarization Analysis
Data mining process models
The data mining process is divided into two parts i.e. Data Preprocessing and Data Mining. Data Preprocessing involves data cleaning, data integration, data reduction, and data transformation. The data mining part performs data mining, pattern evaluation and knowledge representation of data.
Cross-industry standard process (CRISP)
Cross-industry standard process for data mining, known as CRISP-DM, is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics model, it consist of 6 stages as shown below;
- Project Goal Setting
- Understand The Data
- Data Gathering & Preparation
- Data Modeling
- Data Evaluation
- Deploy The Solution
Project Goal Setting
Setting your goals and allows teams to assign roles and make a clear plan to move forward and achieve success. Expectation management is key to avoiding issues throughout the data mining process.
Understand The Data
Figure out what kind of data is needed to solve the issue, and then collect it from the proper sources
Data Gathering and Preparation
The data gathering and preparation stage is all about making sure that the data is usable. This step helps to avoid gathering wrong or inconsistent data to ensure proper a smooth campaign.
Data Modeling
Data modeling in software engineering is the process of creating a data model for an information system by applying certain formal techniques
For Data Modeling to work, it needs to have quality data, security procedures, consistent semantics, default values, and naming conventions. There are two types of Data Modeling Techniques this include;
- Entity-Relationship (E-R) Model
- Unified Modeling Language (UML).
Data Evaluation
After you have successfully modeled your data, the data is analyzed, it is then extracted, transformed, and visualized. Data analysis helps bring together useful information to give insights or test hypotheses.
Deploy The Solution
Deployment is simply the testing or the use of the analyzed data. There are four different types of model deployment;
- Data science tools
- Programming language
- Database
- SQL script or predictive model markup language.
SEMMA (Sample, Explore, Modify, Model, Assess)
The acronym SEMMA stands for Sample, Explore, Modify, Model, Assess, and refers to the process of conducting a data mining project.
Compared to CRISP-DM, SEMMA is even more narrowly focused on the technical steps of data mining. It skips over the initial Business Understanding phase from CRISP-DM and instead starts with data sampling processes. SEMMA likewise does not cover the final Deployment aspects
This methodology has 5 steps namely;
- Sample Data
- Explore Data
- Modify Data
- Model Data
- Assess Data
Sample Data
Here a huge chunk is extracted and a sample that represents the entire data is taken.
Explore Data
The sample extracted is explored to discover more information on the sample.
Modify Data
Here the data gotten from the sample is the modified to make more sense and easily understood.
Model Data
In the light of research and modification, the models that explain the patterns that appear in data are built.
Assess Data
The usefulness and accuracy of the model are evaluated during this stage in the KDD process in data mining. The test of the model against actual data is conducted in this step. The data is made accessible.
