Data Mining is to intelligently discover useful information from large amounts of data to solve real-life problems. It is a combination of two words: data and mining. Data is a collection of instances, and mining is designed to filter useful information. Data mining, called knowledge discovery in databases (KDD), is responsible for analyzing data from different perspectives and classifying them. There are many data mining techniques used to transform raw data into useful data. It has various applications such as detecting anomalous behavior, detecting fraud and abuse, terrorist activities, and investigating crimes through lie detection. Data mining can offer many benefits by improving customer service and satisfaction, and lifestyle, in general. Data mining is present in many aspects of our daily lives, whether we realize it or not. It affects how we shop, work, what we search.
Importance of data mining:
- It helps in exploring the large increase in the database and gather only valid information by improving segmentation.
- It’s an efficient, cost-effective solution by uncovering the risk and fraud that makes profitable production.
- Sometimes customers having difficulty while purchasing helps in decision making and increases the sale.
- Data mining techniques can help organizations in real-time plan and save time.
- Also, saved money through fraud detection.
Application area of data mining:
- Future Healthcare
- Market Basket Analysis
- Manufacturing Engineering
- Fraud Detection
- Intrusion Detection
- Customer Segmentation
- Financial Banking
Data mining architecture:
Data mining architecture unveils how data extraction falls out. The architecture consists of various steps i.e Data source, data mining engine, data warehouse server, pattern evaluation, user interface, and knowledge base.
How data mining influences privacy, security, and socially:
Security and privacy have been an initial concern all the time. It aimed at future predictions using previous data. Suppose we buy any product so based on past purchases they make predictions and which also target our personal information. The continuous development of data mining techniques brings serious threats to data security and privacy which is very important to protect. The real threat is that if information gets exposed to unauthorized parties, it will be impractical to stop misuse. Therefore, we must need a system that possesses to protect data and its resources concerning authenticity and integrity.
How we can protect our data:
- Due to minimal protection setup, we lose data so we need to initiate a multilayer security system
- Access Controls are only given to those who have been given the authorization can access the data
- Data must verify an individual user’s identity
So, some privacy preservation methods protect sensitive or private data while allowing useful information to be extracted from the data set.
- Privacy-Preserving Data Mining (PPDM): The main objective of the PPDM is to protect the privacy of the data and extract only relevant information. It ensures the protection of individual data to conserve privacy and provide accuracy by performing all the data mining operations.
Techniques of PPDM is further divided into various categories:
- Data Hiding Technique: In, this technique the data is reform in such a way that the sensitive or private information will not be visible to other parties. Using various ways we can implement these techniques such as Cryptographic Technique, Data Perturbation, and Anonymization Technique.
- Knowledge hiding Technique: In this technique, sensitive content is extracted from data using a data mining algorithm. There are different ways of implementing these techniques such as Association Rule Hiding, and Query Auditing.
- Hybrid Technique: It is a combination of the two techniques which infuse the limitations of the above two techniques.
Social Impacts of Data Mining:
Data mining has innovatively influenced our daily lifestyle like how we work, shop, what we buy, search for any information, importantly saves our precious time and offers personalized product recommendations based on our previous history like amazon, Flipkart, etc.
Data mining emerging in all fields like Healthcare, Finance, Marketing, and social media. But there is a higher contribution towards healthcare and well-being by using data mining software to analyze data when developing drugs and to find associations between patients, drugs, and outcomes. And improving patient satisfaction, providing more patient-centered care, and decreasing costs, and increase operating efficiency and Insurance organizations can detect medical insurance fraud and abuse through data mining and reduce their losses.
An old payment system has now taken different forms of transactions depending on usage, acceptability, methods, technology, and availability. It changes the physical financial transactions to virtual payment transactions. So, data mining focuses on successful transactions and keeps track of fake transactions.
It is also used in Web-wide tracking technology that tracks user’s interests while visiting any site. So, information about every site is been recorded, which can be used further to provide marketers with information reflecting your interests.
It is also used for customer relationship management which helps in providing more customized, personal service to individual customers. By studying browsing and purchasing history on Web stores, companies can tailor advertisements and promotions to customer profiles, only those who are interested and less likely to be annoyed with unwanted mailings. This helps in reducing costs, the waste of time, and improving work productivity.
Advantages:
Improved security: Data mining can help identify patterns and anomalies that could indicate security breaches, enabling organizations to take action to prevent future attacks.
Personalized services: Data mining can enable personalized services, such as customized product recommendations or personalized healthcare treatments, that can improve the overall customer experience.
Improved decision making: Data mining can provide insights into customer behavior, market trends, and other important factors, enabling organizations to make more informed decisions.
Improved efficiency: Data mining can help streamline business processes, enabling organizations to operate more efficiently.
Disadvantages:
Privacy concerns: Data mining can raise privacy concerns, as it involves collecting and analyzing data about individuals without their explicit consent. This can result in the disclosure of sensitive information, which can have negative consequences for individuals.
Security risks: The data used in data mining can be subject to security risks, such as unauthorized access or hacking, which can result in the exposure of sensitive information.
Bias: Data mining algorithms can be biased, which can lead to discriminatory outcomes or reinforce existing biases.
Social impacts: Data mining can have social impacts, such as increasing surveillance and reducing personal autonomy, which can have negative consequences for society as a whole.
Conclusion:
Data mining has several advantages and disadvantages when it comes to privacy, security, and social impacts. While it can provide valuable insights and improve efficiency, it is important to carefully consider the potential risks and impacts before implementing data mining practices. Organizations must take steps to mitigate risks and protect the privacy and security of individuals, while also ensuring that data mining is conducted in an ethical and responsible manner.