Data Mining is gaining popularity among various industries as it extracts valuable information from the data and helps the organization build strategic decisions based on the data. Data Mining is a broad term and is classified into different categories.
Businesses are creating and keeping a great amount of data these days in order to analyze and develop insights in order to improve processes, decrease costs, and engage better with customers, among other things. Data mining, which is simply the process of sifting and sorting through data to find underlying trends and patterns, is what makes these commercial insights possible.
In business, Data Mining is the process of extracting meaningful information from raw data by detecting hidden patterns and trends. Businesses can use a variety of methods to parse enormous data volumes in batches and extract crucial information. This data then aids firms in fine-tuning plans, increasing revenue, lowering costs, enhancing customer connections, mitigating risks, and much more.
This blog will discuss two categories of Data Mining, Descriptive Data Mining and Predictive Data Mining and the differences between them.
Table of Contents
What is Data Mining?
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Data Mining is a process of finding patterns and extracting useful information from the pool of large data sets by transforming the data with many business rules. With the help of Data Mining procedures, Raw datasets are converted into valuable datasets, which developers can further use to analyze and determine the patterns.
Data Mining is an effective procedure for any organization as it helps improve the marketing strategies and helps them target the customer base based on the data. The help of structured data also allows you to study different aspects of data and then get more innovative ideas to increase productivity and sales.
The Data Mining process breaks down into the following steps:
- Collect, Extract, Transform and Load the data into the Data Warehouse.
- Store and manage the data in the database or on the cloud.
- Provide access to data to the Business Analyst, Management Teams, and Information Technology professionals.
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What is Descriptive Data Mining?
As the name suggests, Descriptive Data Mining provides the descriptive information from the data. It is often used to provide correlation, cross-tabulation, frequency, etc., from the data. Once the data is captured, it is then modified into human interpretable form to establish co-relations between a set of items.
Descriptive mining informs about data regularities/irregularities and reveals a certain pattern of data behavior. With the help of this information, data can be converted and translated for reporting and monitoring.
What Can Descriptive Analytics Tell Us?
As the name suggests, Descriptive Data Mining provides descriptive information about the data. Descriptive Data Mining can help us in various fields; some of the examples are:
- Compare the pre and post-assessment of the test.
- Collecting the survey and analyzing the people’s opinions.
- Understanding the total stock in inventory.
- Finding the average dollars spent per customer and year-over-year change in sales.
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Descriptive Data Mining Models
Clustering
Clustering is a technique widely used for exploring Descriptive Data Mining. A cluster is a collection of objects or rows similar to one another. A good data cluster ensures that the inter-cluster similarity is low and the intra-cluster similarity is high. The clustering method plays a pivot role in determining the high-quality data cluster.
Data Clustering can also be used as a preprocessing step to identify the groups to build predictive models.
For example, a dataset containing age and salary can be clustered like :
If AGE >= 25 and AGE <= 40 and
SALARY >= 50000 and SALARY <= 70000
then CLUSTER = 10
The various methods to create clusters are as follows:
- K-means algorithm
- O-Cluster (Available in Java)
The various uses of clustering in Descriptive data Mining are:
- Market Segmentation
- Social Network Analysis
- Search Result Grouping
- Medical Imaging
- Image Segmentation
- Anomaly Detection
Association
Association Data Model is associated with the market-basket analysis. It is mostly used to discover the relationship or correlations between items. In Association models, the items are linked with each other. This association helps determine the chance of using another item against one. For example “People who buy noodles also buy garlic bread or ketchup.”
Association Rules can be applied with the following principle in mind:
- Support: How often do these items occur together in the data?
- Confidence: How confident we are that the associated items occur together.
- Value: What is the business value of the item associations.
Feature Extraction
Feature Extraction is the process that creates new features from the existing features and then later discards the original features aiming to reduce the number of features.
These new features are created based on combining similar features into one feature and then are used to perform Descriptive Data Mining on the data.
Various methods of Feature Extraction:
- Principle Components Analysis (PCA)
- Independent Component Analysis (ICA)
- Linear Discriminant Analysis (LDA)
- Locally Linear Embedding (LLE)
- T-distributed Stochastic Neighbor Embedding (t-SNE)
What is Predictive Data Mining?
Predictive Data Mining, as the name suggests, is used to predict a future event or data trends based on the past behavior of the data. Predictive data mining provides predictive analytics, which is used to predict the outcomes from the data.
The main aim of Predictive Data Mining is to predict the future behavior of the data by using supervised Machine Learning techniques.
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Predictive Data Mining Models
The basic approach or Data model for Predictive Data Mining is:
Classification
Classification is the act of assigning objects to one of several predefined categories within the data. Classification is also defined as a learning function of a target function that sets each attribute to a predefined class label.
A classification task begins with building data (training data) for which the target values (or class assignments) are known.
Various classification algorithms are used to find the relationship between the source and target attributes.
Time-Sequence Analysis
The Time Sequence analysis on Predictive Data Mining evaluates the sequence of data points or events based on time. A classic example of Time Sequence analysis is eCommerce stock inventory. The number of items sold in the last four months is used to predict the demand for that item over the next week, month, or the rest of the year.
Regression
The Regression Technique is used for the data that verifies the data value for a function. There are two types of Regression:
- Linear Regression: is an algorithm that looks for the optimal line fit between the two attributes so that one can be applied to predict the other.
- Multi-Linear Regression: Regression involves two or more two attributes, and data are fit to multidimensional space.
Example of Predictive Data Mining
In Predictive Data Mining, one of the most common use cases is to generate a credit score for users. Financial institutions use the credit score to determine the probability and trust of a customer who makes credit card payments on time. By analyzing their past behavior, a score is generated which reflects:
- The trust in the customer.
- Their spending pattern and bill payments.
Based on this information, the credit score can increase or decrease for that particular customer, indicating that the customer can be relied on for more credit or not.
Difference Between Predictive and Descriptive Data Mining
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| Base for Comparison | Descriptive Data Mining | Predictive Data Mining |
| Concept | Descriptive Data Mining provides descriptive information about the past behavior of the data by analyzing it. | Predictive Data Mining determines or predicts the future behavior of the data based on its records. |
| Accuracy | Descriptive Data mining provides the accurate results on the data behavior | Predictive Data Mining provides the future outcome of the data but does not claim its accuracy. |
| Data Methods | Descriptive Data Mining requires Data Mining and Data Aggregation | Predictive Data Mining requires Statistical and Forecasting methods. |
| Data Models | Descriptive Data Mining is based on data classification, association, and feature extraction to report the past behavior of the data. | Predictive Data mining is based on data classification, time series analysis, and data regression to understand the data and predict future events. |
| Type of Approach | Descriptive Data Mining is a Reactive Approach. | Predictive Data Mining is a Proactive approach. |
| Questions (or Way of Approach) | Descriptive data mining approaches with the following questions in general –
What Happened?Where is the problem?What is the frequency of the problem? | Predictive data mining approaches with the following questions in general –
What would happen next?What would be the outcome if the trends continued?What actions need to be taken? |
| Benefits | Descriptive Data Mining answers many of the most common questions about business performance, such as whether the last quarter’s sales were in line with goals. | Predictive Data Mining helps in reducing time, effort, and costs in forecasting business outcomes. |
Conclusion
In this blog post, you have learned Descriptive Data Mining and its use. You also learned about Predictive Data Mining and compared them against various factors.
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