A Simple Guide To KDD Process in Data Mining

EM Admin
Mar 9, 2020
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KDD stands for Knowledge Discovery in Databases. It is another name for data mining.

So! What data mining is.

It is actually a collection of processes, which helps in drawing intelligence. Or simply put, feasible decision-making to achieve desirable goals is possible through it.

This practice is extremely useful when you draw decisions via the procedure of data science. You just have to sort through a large volume of datasets. Once done, filter some patterns that are relational and concerned with business problems. Analysing them helps in figuring out such patterns that are informative. They prove a building block in analyzing and making more-informed decisions. You can easily predict what trend is likely to be popular.

Why do you Require the Mining of Data?

The data is frequently mounting through sensors, images, social media, videos, etc. We need to seriously understand what it states. For this purpose, the knowledge discovery process is the best fit. It actually makes decision-making easier.

This process helps in effectively analyzing the collected data. The result of that analysis is called business intelligence. Certainly, reaching out to feasible solutions involves the in-depth analysis of historical data and real-time analytics applications. With their support, you can easily hear the voice of data.

In the nutshell, it helps in planning business strategies and managing operations. These two goals involve marketing, advertising, sales, and customer support. Besides, manufacturing, supply chain management, finance, and human resources functions can be transformed through it. In other words, you can apply it in different industries and their operations for refining.

It actually helps in determining fraud patterns, risks, cybersecurity flaws, and customer behavior. Once discovered, making critical decisions seems like a walkover.

How Does Knowledge Discovery Process (KDD) Work?

This process requires some manual and digital resources. For brainstorming, data scientists, skilled BI & analysis experts, and researchers are required. The tools and methods would depend on the size, type, and ultimate expectations from this knowledge mining process. Later on, the driven results may become machine learning and artificial intelligence. It completely depends on the purpose of why data mining is required.

Steps of Knowledge Discovery Database Process

Let’s get through the steps of the Knowledge Discovery Process (KDD).

Step 1. Data Collection

For any research, you need some information. The team of researchers finds relevant resources to extract, convert, and process records from different websites or big data environments like data warehouses, lakes, or servers. It may have structured and unstructured files. Sometimes, the structured database gets errors because of data migration, and the inability to show compatibility with the format of files.

Here, the errors should be removed at the point of entry to get off wrong or inaccurate data entry.

Step 2: Preparing Datasets

Once collected, explore datasets. Prepare their profiles during pre-processing. It is actually, the transformation step. Herein, all records are refined, keep consistent, and remove oddities. This is how resources are prepared for effective data analysis. Then, the error-fixing would be remaining, which takes place in the next step. Data mining experts at Eminenture follow the same procedure.

Step 3: Cleansing Data

Cleansing here means removing noises and redundancies from data collection. Noises refer to corrupt, incomplete, dupes, and other oddities in the database. For neat and clean databases, the following subsets are followed:

De-duplication
Data appending
Normalization
Typos removal
Standardization

Step 4: Data Integration

This process requires combining data from multiple sources. This process of knowledge discovery (KDD) involves various tools for migration & synchronization and applications.

Mainly, this process involves extracting, loading, and transforming datasets. It is also called the ETL process. For research, the information must be:

Extracted
Transformed
Loaded for deep analysis

Step 5: Data Analysis

Getting deep into insights is called analysis. Herein, datasets are filtered for processing. They are thoroughly observed to identify if they can be useful and the perfect fit for these processes:

Neural network
Decision trees
Naïve Byaes
Clustering
Association
Regression, etc.

Step 6: Data Transformation

Data mapping for assigning elements from sources to destination OCR conversion for translating data via scanning, recognition, & conversion
Coding for transforming the entire data into the same format

This step is dedicated to converting data into an identical structure and format for later mining data. Sometimes, PDFs or mixed forms of data are availed. Converting them into digital is a must. So, the following procedures are carried out:

Step 7: Modeling or Mining

This is the most crucial step, which involves applications and scripting to extract relevant patterns to complement objectives. It covers

Transitioning of data into models or patterns
Verification to determine the accuracy of facts

Step 8: Validating Models

Also called evaluation, this step of the KDD process involved validation of identified patterns. The data scientist prepares some predictive models (or predictions) to compare and validate if they are able to provide expected results. Validation proves that the models actually fit the predictive models using classification and characterization or the best-fit method.

This process includes:

Finding the interestingness rating or score of each model
Summarisation of the pan database
Visualisation to easily understand and analyse

Step 9: Knowledge Presentation

As its name suggests, this step of the data mining process requires you to present findings in comprehensive datasets. Some visualisation tools can make it easier like data studio.

Create reports
Set discriminant rules, classification rules, characterization rules, etc.

Step 10: Execution

Finally, the driven decisions or results are applied to various applications or machine learning. Once done accurately, it becomes Artificial intelligence or AI for automating processes.

This is how the KDD process works and intelligence takes birth. Or, you can prepare a full-proof business plan with the assurance of success.