Good Morning Viewers Welcome to y second blog on Data Mining Applications and KDD. Let me tell you that KDD in databases, is a technique for extracting useful information and patterns from a raw database so that they can be applied to various fields or applications.
The above sentence provides an overview or idea of KDD, however, the process is extensive and complex and requires numerous steps and iterations. Let’s try to set the tone with an example before we go into the specifics of KDD.
Table of Contents
What is KDD?
KDD is a programmed and analytical method for modeling data from a database to extract relevant and meaningful “knowledge” in data mining. Data mining is the foundation of KDD and is therefore essential to the entire methodology.
It makes use of a number of self-learning algorithms to extract insightful patterns from the analyzed data. There are numerous iterations between the various processes in this closed-loop, constant feedback process as required by the algorithms and pattern interpretations.
Understanding of goal-setting and application
This is the initial phase in the procedure, which calls for prior comprehension and expertise in the area to be applied in. In this step, we choose how to extract knowledge from the processed data and the patterns identified by data mining. This fundamental assumption is crucial and, if it isn’t made correctly, can result in erroneous interpretations and detrimental effects on the end user.
Data Integration and Choices
The data must be chosen and separated into meaningful sets based on availability, accessibility, significance, and quality once the goals and objectives have been established. Because they serve as the foundation for data mining and have an impact on the types of data models that are created, these characteristics are essential.
Data Preparation and Cleaning
In order to increase the data set’s accuracy and dependability, this stage includes looking for missing data as well as deleting noisy, redundant, and low-quality data. Based on application-specific criteria, specialized algorithms are utilized to search for and remove undesirable data.
Transformation of Data
The data is prepared in this step so that the data mining algorithms may use it. As a result, the data must be in aggregated and consolidated forms. On the basis of functions, qualities, features, etc., the data is consolidated.
You can read my blog on Data Transformation
The foundational or core process of the entire KDD is this. In order to create prediction models, algorithms are utilized here to extract useful patterns from the altered data. With the aid of artificial intelligence, sophisticated numerical and statistical approaches, and specialized algorithms, it is an analytical tool that assists in identifying trends from a data set.
Pattern Anomaly Detection
To investigate the effects of data collected and modified during earlier processes, the trend and patterns must be displayed in discrete forms, such as bar graphs, pie charts, and histograms, after being identified by various data mining methods and iterations. This aids in assessing a given data model’s efficacy in light of the domain.
Knowledge Acquisition and Application
This is the last phase in the KDD process, and it calls for applying the “knowledge” that has been extracted from the previous step to the particular application or domain in a visual format like tables, reports, etc. The decision-making process for the aforementioned application is driven by this stage.
Data Mining Applications
Data mining techniques are used in a range of industries, including banking and finance as well as healthcare. To highlight the qualities of data mining and its five applications, we have chosen the best of the best.
Data mining techniques have the ability to dramatically change the healthcare system. It can be used to find best practices based on data and analytics, assisting healthcare facilities in cutting costs and enhancing patient outcomes. Data mining can be used to change things, together with machine learning, statistics, data visualization, and other methods. When predicting patients from several categories, it can be useful. This will make it easier for patients to get critical care when and when they need it. Healthcare insurers can spot fraudulent activity with the aid of data mining.
The application of data mining techniques in education is still in its development. It tries to create methods for exploring knowledge using data from educational environments. These methods are anticipated to be used for a variety of objectives, including researching how educational support affects students, assisting students with their future learning requirements, and advancing the science of learning. These methods can be used by educational institutions to not only forecast student performance on exams but also to make informed choices. These institutions can concentrate more on their methodology of instruction with this knowledge.
Market basket analysis
This modeling approach has a hypothesis-based foundation. According to the hypothesis, it is very likely that you will buy products that don’t belong to the group you typically buy from if you buy specific products. This method can be used by retailers to learn more about their consumers’ purchasing preferences. Retailers can utilize this information to alter their store’s layout, which will make customers’ shopping much simpler and faster.
Customer relationship management (CRM)
Customer relationship management (CRM) entails finding new consumers and retaining existing ones while enhancing customer loyalty. Every company requires customer data in order to evaluate it and use the results to develop enduring relationships with their clients. They can achieve that with the aid of data mining.
Applications of data mining in CRM include:
- Sales Forecasting
- Market Segmentation
- Identifying the loyalty of customers
Finance and banking
The banking system has been witnessing the generation of massive amounts of data from the time it underwent digitalization. Bankers can use data mining techniques to solve the baking and financial problems that businesses face by finding out correlations and trends in market costs and business information. This job is too difficult without data mining as the volume of data that they are dealing with is too large. Managers in the banking and financial sectors can use this information to acquire, retain, and maintain a customer.
Fraudulent activities cost businesses billions of dollars every year. Methods that are usually used for detecting fraud are too complex and time-consuming. Data mining provides a simple alternative. Every ideal fraud detection system needs to protect user data in all circumstances. A method is supervised to collect data, and then this data is categorized into fraudulent or non-fraudulent data. This data is used in training a model that identifies every document as fraudulent or non-fraudulent.
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