What is Data Mining and how does it work?

Data mining is the process of finding patterns in large data sets.

Data mining tools enable organizations to extract information from these huge datasets and identify relationships among variables.

The goal is to make faster, better, or longer-term decisions based on what has been learned about the data.

In this article, you will learn about data mining in detail, how it works, uses and its benefits etc.

So let’s get started!

What is Data Mining

Data mining is the extraction of knowledge from data. Data mining is a process that extracts these patterns and trends by selecting specific data sets that are important, then performing correlations and statistical analysis to identify relationships and trends that would otherwise be difficult to spot with unaided human intelligence alone.

Data mining is a very broad field, as almost any type of data can be mined.

In the past, data mining was used to extract patterns from spreadsheets or databases where users have been able to define their own queries using SQL.

In the last decade, with the advent of new sources for information such as text files and XML data files, as well as more sophisticated search capabilities that allow free-form querying by non-experts, many problems in knowledge discovery can be solved by data mining.

Common sources of raw data include transaction logs, web pages, interaction logs and even email messages.

How Data Mining works?

Data mining is the process of extracting information from a large number of records and databases in order to learn patterns and trends.

A data miner “mines” through data sets and looks for specific types of data, such as customer buying patterns or likelihood that a particular advertisement will result in sales. Big data, which is often unstructured or can’t be easily processed by traditional techniques, has spiked interest in data mining opportunities.

Data mining is closely related to data or knowledge discovery. It can also be considered a subset of artificial intelligence and machine learning. Data mining uses specific algorithms, building on techniques like: association rules, clustering and classification to discover patterns in large data sets.

Data mining deals with both types – structured and unstructured – but it is the unstructured data that presents more of an obstacle.

Data mining can be considered as a sub discipline of computer science, statistics or business intelligence. While the terms “data mining” and “knowledge discovery in databases (KDD)” are used interchangeably, KDD focuses on the creation of models that can be extracted from large data sets.

The process of data mining includes three main steps:

1). Data cleaning and integration

2). Data selection or pre-processing

3). Predictive modeling or inference.

Data Cleaning – It is the first step in data mining project wherein the input data must be cleaned to get rid of incorrect, incomplete, irrelevant, redundant and garbled records. Data is also checked for plausibility. The data sets can be cleaned manually or by automated software packages.

Data Selection – In this step the user specifies which data is relevant to their goals. They decide on what they want out of the data mining project and select only those fields that could help solve a particular problem. Data is organized based on mining requirements and a reduced data set is prepared that can be used to create a model.

Predictive Modeling – In the last step, the reduced data set with fewer attributes is fed into a predictive model which generates results. Thus, this final stage harnesses specialized software to separate useful information from raw data.

Advantages of Data Mining

A lot of data analysis and time can be reduced.
Data mining is effective in detecting patterns and anomalies.
Data mining is helpful in forecasting future trends.
Data mining is a powerful tool in understanding how customers behave.
Data-mining is an important tool for locating and tracking the risk factors in the business environment.
Data-mining helps in improving customer service.
It increases efficiency and profitability for the company.
It helps in boosting morale and productivity of employees.
Data mining can be helpful to understand consumer buying patterns.
It is useful in making critical business decisions.
It helps in making more accurate sales forecasts.
A lot of data analysis and time can be reduced.
Data mining is helpful in forecasting future trends.
Data mining is a powerful tool in understanding how customers behave.
Data mining is an important tool for locating and tracking the risk factors in the business environment.
It increases efficiency and profitability for the company.
It helps in boosting morale and productivity of employees.

Data Mining Use Cases and Examples

Data mining is used across many disciplines to explore all sorts of data.

With a few clicks, data miners can instantly access vast quantities of data and apply a variety of analytical techniques to them. Some common use cases include:

Researching a company’s market share, the competitive landscape, or customer sentiment by analyzing their social media presence.
Detecting incidents or anomalies in surveillance footage or sensor data.
Designing more effective treatments for illnesses based on molecular interactions between drugs and patient responses.
Identifying potential financial risks from insurance claims or trading data.
Calculating the best routes throughout a transportation network to minimize traffic and fuel costs.

Data Mining Techniques

Below, you will find a list of different technological data-mining techniques to use when creating interactive visualization that allows for users to explore findings.

1. Clustering Algorithms

There are many different clustering algorithms being used today in data mining for data analysis to gain a better understanding of groups within a dataset. These include k-means, hierarchical, biclustering, and several other techniques that can be used either independently or combined with one another.

2. Classification Analysis

Classification analysis is an extremely common type of data mining that can be used on datasets with categorical variables.

Regression analysis has some limitations when it comes to predicting future events because the continuous nature of regression models doesn’t take into account the ordering of categories.

If you are trying to predict outcomes that have categories that are ordered or have a natural order, you can use classification with its categorical outputs and discrete categories. This means it is possible to correctly predict what category an observation belongs to in the future.

3. Factor Analysis (FA)

Factor analysis is a statistical method that can be used to understand the correlation between different variables.

It is often done with categorical variables and it results in a smaller set of new variables called factors, which are linear combinations of your original variables. Factor analysis can help classify data and make predictive models more accurate by removing noise from the input dataset.

4. Neural Networks

Neural networks, a subset of machine learning algorithms, are used to model complex non-linear relationships between inputs and outputs using an interconnected group of nodes, which resemble a network of neurons in the brain.

In this day and age neural networks can also be used to learn from large datasets extremely fast and accurately, which makes it an appealing option when analyzing data.

5. Regression Analysis

Regression analysis is a statistical technique that can be used to help explain the relationship between two or more variables, where one of those variables is typically continuous and the other one is either categorical or continuous.

There are several types of regression models we could talk about here, including linear regression and non-linear regression, but we will focus on linear regression since it is one of the most commonly used types of models.

6. Decision Trees

Decision trees are a type of predictive model that can be used to predict the outcome of a categorical dependent variable (also known as an outcome) based on multiple input variables (categorical and/or continuous). Decision trees can be used to make predictions for classification and regression models.

7. Association Rules

Association rules are a type of data mining algorithm that can be used to identify relationships between items in transactional datasets, which makes it useful in market basket analysis.

It is commonly applied to customer transaction data, such as purchasing behaviors at a grocery store or shopping cart data at an online retailer.

8. Sequence Prediction Algorithms

Sequence prediction is a type of data mining that is typically applied to sequential data such as time series and biological sequences.

In the time series example from the previous section, you could use a sequence prediction algorithm to predict future sales based on historical sales data.

9. Natural Language Processing

Natural language processing (NLP) is a type of data mining that can be used to identify and extract information from unstructured text such as customer reviews or social media posts, and classify it into predefined categories.

NLP makes it possible to define and implement a text mining solution to a variety of business problems.

10. Image Recognition

Image recognition is a type of data mining that can be used to identify and distinguish objects in images, such as faces in family photos or components of an engine in a car photo.

It is most commonly applied to image management problems, such as photo tagging and product identification, but the technology also has the potential to be applied to other business problems.

11. Sentiment Analysis

Sentiment analysis is a type of data mining that can be used to automatically identify and understand customer opinions or sentiments expressed in texts such as reviews or social media posts.

It makes it possible to analyze broad trends about a product or service and make predictions about the customers’ overall opinions.

12. Time Series Prediction

Time series prediction is a type of data mining that can be used to predict future values in time series by making use of historical observations.

It is most commonly applied to time series data involving customer behavior, such as predicting quarterly sales based on historical sales data.

13. Text Mining

Text mining is a type of data mining that can be used to identify and extract information from unstructured text such as customer reviews or social media posts, and classify it into predefined categories. It makes it possible to define and implement a text mining solution to a variety of business problems.

Why is Data Mining important?

Data mining is important because it provides a systematic way of searching huge amounts of data, creating usable, actionable insights.

It has positively impacted businesses across the spectrum for over a decade by locating hidden patterns in management and customer data to improve operations and identify new opportunities.

What is another term for Data Mining?

Knowledge Discovery in Data (KDD) is another name for data mining.

Data Mining vs Data Warehousing

Data mining and data warehousing are two terms that are often used interchangeably. However, they are not the same thing.

Data warehousing is a process of storing data in an organized fashion for future retrieval and analysis purposes. There are various purposes behind data warehouses, which range from personal to business use.

Data mining is a process of extracting hidden patterns from large amounts of data consisting either of structured or unstructured contents. It is used for generating information from both qualitative as well as quantitative content.

Data Mining Applications

Data mining applications can be classified as follows:

Predictive data mining: These types of applications are used for predicting events before they happen. Decision trees and neural networks are the most common examples of predictive data mining applications.

Descriptive data mining: These types of applications summarize and understand patterns and relationships in a dataset. Classification and clustering are the most common examples of descriptive data mining applications.

Prescriptive data mining: These types of applications analyze a dataset, learn from it and then recommends what to do next. In other words, prescriptive data mining applications recommend an answer based on its analysis. Recommender systems are the most common example of prescriptive data mining applications.

Anomaly detection: These types of applications identify unusual patterns in a dataset. Fraud detection is the most common example of anomaly detection applications where it’s used to detect credit card fraud. Other examples include intrusion detection, failure prediction and fault diagnosis.

Associative data mining: These types of applications mine data that describes relationships between different attributes. Market-basket analysis and web usage mining are the most common examples of associative data mining applications.

Graph data mining: These types of applications analyze a graph to extract useful information from it. Social network analysis is the most common example of graph data mining application.

Text Data Mining: These types of applications are used for analyzing text documents. Text mining applications help us understand the meaning, structure and useful information present in a text document. Market basket analysis is one example where this technique is used to find relationships among different products purchased by customers.

Who uses Data Mining?

The number of businesses that use data mining is growing, and it is a popular trend. Some of the most notable companies that are using data mining to better their business include Amazon, AT&T, IBM, Marriott International, Netflix, Nike, Toshiba Corporation.

The Future of Data Mining

Data mining has revolutionized today’s world. There are several discussions about what will happen with data mining in the future.

Some people think that predictive analysis will be better than other methods while others see it as just another tool in statistician’s toolkit. Whatever trends occur in the future, one thing is for sure: this technology will continue to change our world.

What does the scalability of a Data Mining method refer to?

Scalability refers to both distributed data processing and dynamically provided services to users. The system can handle a large number of users and a large amount of data.