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What Is Data Mining?
Data mining is the key part of Artificial Intelligence. It is a process of detecting aberrations, models, and relationships within large data sets. By identifying such components, the target is to predict outcomes. We can use such data to improve profits, cut expenses, enhance client relationships, decrease risks, and many other business-oriented decisions using various data mining techniques.
In other words, we can say that it is the process of analyzing massive volumes of data. It tries to discover business intelligence and helps companies solve various problems. This process needs cleaning through huge masses of material to discover hidden patterns or information present in the data.
Objectives of Data Mining
Data mining tries to discover the exact structure inside unstructured data. It extracts the real business meaning from noisy data. The major part is to discover patterns in random data. Finally, use such information to build machine-learning models to better understand trends, patterns, relationships, and future estimates. As a result, it can predict customer behavior, market and competition trends, etc.
Data Mining Concepts
● Data cleansing & preparation
It is the first step where data is modified or cleaned and make a suitable form of it. It is a core step for further analysis, processing, and model building.
● Artificial intelligence (AI)
AI is nothing but the analytical activities that are associated with human intelligence. For example - Planning, Learning, Reasoning, problem-solving, etc.
● Association rule learning
It is also known as market basket analysis. It tries to search for relationships among all variables in a dataset.
● Clustering
It is a process of pre-step related to machine learning. It splits a dataset into a set of meaningful subclasses. Here the sub-class is known as clusters.
● Classification
It is a binary type of problem and acts on the target variable of the dataset. The intent is to predict the target class for each case (Yes/No) in the data.
● Data analytics
It is the process of estimating digital data into useful market statistics.
● Data warehousing
It is like the storage of data that is used to help the business to make decisions. It is the best way to deal with large-scale data mining problems.
● Machine learning
It is a process of ability to learn with the help of statistical techniques without being explicitly programmed.
● Regression
It is a process to predict a range of numeric values. It could be sales, temperatures, or stock prices, etc.
Types of Data Mining
Data mining has several types. Those are given below:
- Pictorial data mining
- Text mining
- Social media mining
- Web mining
- Audio and video mining
Benefits of Data Mining
- It helps companies to obtain reliable data.
- It is an efficient and cost-effective solution linked with other data applications.
- It assists businesses to make successful production and adjust based on its operations.
- Data mining works on both new as well as legacy methods.
- It helps companies make informed determinations.
Data Mining Applications
Data mining is used in numerous sectors. All details are given below:
● E-Commerce: - This type of company provides products to end-users. So, Recommendations systems play a vital role here. Media-service providers, Social applications, and online retailers use it widely. Such companies are Amazon, Flipkart, etc. The target is to predict consumer behavior and offer the most reliable service to enhance the client experience.
● Banking: - Bank generates huge amounts of data on their day-to-day based transactions. The target is to identify likely insolvents to determine whether to approve loans, credit cards, and other stuff.
● Retail: - Various Retail shops use data mining techniques. For example-supermarkets, grocery stores, Laptops, and mobile shops. Their target is to identify consumer behavior. It also encourages business owners to come up with good proposals to enhance the client's spending.
● Education: - Based on the student’s Data Analysis , institutions try to identify the low performers. Based on the result, an institution can give extra attention to such students.
● Healthcare: - In healthcare, data mining is also used to identify medical swindles and violations by investigating the medical application patterns.
Topics for Data Mining Assignment
- House price prediction- Data mining project
- Fake news detection data mining project
- Credit card fraud detection
- Movie recommendations system using python
- Detecting Phishing websites using data mining techniques
- Detecting Parkinson’s disease
- Sentiment analysis - data mining project
- Diabetes prediction using data mining
- Handwritten digit recognition
- Intelligent Transportation System
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Data Mining Assignment FAQ
What are the types of Data Mining?
- Integration
- Selection
- Data cleaning
- Pattern evaluation
- Data transformation
- Knowledge representation
What is Data Purging?
Data Purging is a key part of the database management system. It is nothing but a procedure that helps to manage associated data in a database. It leads to the method of cleaning trash data by dropping or removing the useless values of a row or columns. So, we can say that it is a mandatory pre-step before moving to data analysis. We have to clean and validate the data first.
What is the fundamental difference between Data Warehousing and Data Mining?
Data Warehousing is a data extraction technique from diverse sources. Data Mining is an exploring process of the extracted data. Using various query languages, we can achieve this. Finally, we move towards analyzing the outcomes of the business.
What are the key stages of Data Mining?
- Exploration (Data collection)
- Model Building and validation (Machine Learning and Prediction)
- Deployment (Ready to estimate the future)
