As a working professional, you are familiar with terms like data, database, information, processing, etc. You must have also come across terms like data mining and data warehouse. We’ll talk about those two terms in detail later on, but there’s a far more elaborate methodology that encompasses the two terms mentioned above: KDD.
Table of Contents
What is KDD?
KDD is referred to as Knowledge Discovery in Database and is defined as a method of finding, transforming, and refining meaningful data and patterns from a raw database in order to be utilised in different domains or applications.
The above statement is an overview or gist of KDD, but it’s a lengthy and complex process which involves many steps and iterations. Now before we delve into the nitty-gritty of KDD, let’s try and set the tone through an example.
Suppose, there’s a small river flowing nearby and you happen to be either one of a craft enthusiast, a stone collector or a random explorer. Now, you have prior knowledge that a river bed is full of stones, shells and other random objects. This premise is of the utmost importance without which one can’t reach the source.
Next, depending on whom you happen to be, the needs and requirements may vary. This is the second most important thing to understand. So, you go ahead and collect stones, shells, coins or any artefacts that might be lying on the river bed. But that brings along dirt and other unwanted objects along as well, which you’ll need to get rid of in order to have the objects ready for further use.
At this stage, you might need to go back and collect more items as per your needs, and this process will repeat a few times or be completely skipped as per the conditions.
The collected objects need segregation into different types to better suit your application and are further required to be cut, polished or painted. This stage is called the transformation stage.
During this process, you gain an understanding of, for example, where you are more likely to find bigger stones of certain colouration – whether near the bank or deeper in the river, whether the artefacts are probable to be found upstream or downstream and so on. Data mining is an important part when you learn data science.
This helps in decoding patterns which can help in more efficient and quicker completion of tasks. What you eventually end up with is the discovery of knowledge that is refined, reliable and highly specific to your application.
Now, let’s dive into KDD in data mining in detail.
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What is KDD in Data Mining?
KDD in data mining is a programmed and analytical approach to model data from a database to extract useful and applicable ‘knowledge’. Data mining forms the backbone of KDD and hence is critical to the whole method.
It utilises several algorithms that are self-learning in nature to deduce useful patterns from the processed data. The process is a closed-loop constant feedback one where a lot of iterations occur between the various steps as per the demand of the algorithms and pattern interpretations.
Steps Involved in a Typical KDD Process
1. Goal-Setting and Application Understanding
This is the first step in the process and requires prior understanding and knowledge of the field to be applied in. This is where we decide how the transformed data and the patterns arrived at by data mining will be used to extract knowledge. This premise is extremely important which, if set wrong, can lead to false interpretations and negative impacts on the end-user.
2. Data Selection and Integration
After setting the goals and objectives, the data collected needs to be selected and segregated into meaningful sets based on availability, accessibility importance and quality. These parameters are critical for data mining because they make the base for it and will affect what kinds of data models are formed.
3. Data Cleaning and Preprocessing
This step involves searching for missing data and removing noisy, redundant and low-quality data from the data set in order to improve the reliability of the data and its effectiveness. Certain algorithms are used for searching and eliminating unwanted data based on attributes specific to the application.
4. Data Transformation
This step prepares the data to be fed to the data mining algorithms. Hence, the data needs to be in consolidated and aggregate forms. The data is consolidated on the basis of functions, attributes, features etc.
5. Data Mining
This is the root or backbone process of the whole KDD. This is where algorithms are used to extract meaningful patterns from the transformed data, which help in prediction models. It is an analytical tool which helps in discovering trends from a data set using techniques such as artificial intelligence, advanced numerical and statistical methods and specialised algorithms.
6. Pattern Evaluation/Interpretation
Once the trend and patterns have been obtained from various data mining methods and iterations, these patterns need to be represented in discrete forms such as bar graphs, pie charts, histograms etc. to study the impact of data collected and transformed during previous steps. This also helps in evaluating the effectiveness of a particular data model in view of the domain.
7. Knowledge Discovery and Use
This is the final step in the KDD process and requires the ‘knowledge’ extracted from the previous step to be applied to the specific application or domain in a visualised format such as tables, reports etc. This step drives the decision-making process for the said application.
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Conclusion
In today’s world, data is being generated from numerous sources of different types and in different formats, for example, economic transactions, biometrics, scientific, pictures and videos etc. With such huge amounts of information being traded each moment, a technique is of utmost importance which can extract the juice and provide reliable, high quality, and effective data for use in various fields for decision making. This is where KDD is so useful.
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Why is KDD important?
The primary goal of the KDD method is to extract information from massive databases. It accomplishes this by employing Data Mining techniques to determine what is considered knowledge. KDD is defined as a planned, exploratory investigation and modeling of significant data sources. KDD is the systematic process of identifying valid, practical, and understandable patterns in massive and complicated data sets. The base of the KDD method is data mining, which involves the inference of algorithms that analyze the data, build the model, and discover previously unknown patterns. The model is used to extract information from data, and then analyze and forecast it.
Is learning KDD difficult?
KDD is extremely useful in the current technological world. Learning KDD is moderately complex. Learners who want to learn KDD need to learn Computer Science, Statistics, Machine learning, and Data Science. It includes aspects of database and data management, data pre-processing, design and inference factors, relevance metrics, complexity factors, post-processing of discovered structures, visualization, and online updating, in addition to the raw analysis step.
Is data mining also called KDD?
Data mining is the analytical phase of the “knowledge discovery in databases” (KDD) process. Data Mining, which includes the inference of algorithms that examine the data, create the model, and discover previously undiscovered patterns, may also be considered to be at the heart of the KDD method. It is hard to extract implicit, previously unknown, and potentially beneficial knowledge from data using KDD. The examination and analysis of vast amounts of data to uncover valid, unique, potentially valuable, and eventually intelligible patterns in data is known as data mining.
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