Just as miners go underground to search for gold nuggets, data mining is the process of mining large datasets to find relevant information that can be used for specific purposes. Data mining, which is of great interest among the sub-branches of computer science, tends to work mainly on models. After collecting and storing data, the next step is to understand it. Otherwise it is completely meaningless. Data analysis is done in several different ways, such as using machine learning and similar concepts where complex adaptive algorithms are used to manually analyze data. In a more traditional approach, specially trained data scientists can understand complex information and create reports for management evaluation.
Secure and legal data mining is common in many industries, from finance to retail. While surfing the Internet, user data is recorded according to the websites visited, searches made, personal information entered and products viewed. This data is generated by millions of users, then analyzed in detail by the company and used to make smart operational and marketing decisions.
According to companies and needs, data mining shows the possibility of use in many different areas. Some of the usage possibilities are mentioned under the headings below.
Forecasting and Risk Analysis
Analyzing data to determine what didn't go well in the past, such as the number of online visitors who didn't buy after viewing a product, can help retailers better decide what inventory to buy in the future. Similarly, seeing what time of day there was excessive web traffic in the past can help companies prepare by dedicating more resources or investing in server upgrades.
Customer-provided data allows companies to group users in various ways (for example, demographics) by gender, age, income, location and spending habits. In this way, they can effectively provide specific offers or messages to eligible users.
By examining the data, the company can understand what kind of incentives customers are responding to. For example, some groups seem more likely to respond to certain offers or emails at a certain time of the day or week. This information can also clearly explain why users visit a particular website instead of another, or why they stopped purchasing at the last moment. This analysis helps companies determine what steps they can take to prevent negative consumer behavior that harms them.
As a crucial part of advanced technologies such as machine learning, natural language processing (NLP) and artificial intelligence, data mining has advantages in solving company problems, reducing risks and seizing new opportunities. For example, you can use data mining to determine the best time to send reminder emails to potential customers about abandoned shopping carts. By analyzing the data model, you can see that in this case, emails sent after 48 hours have a higher conversion rate than emails sent after 24 hours.
Data mining can easily analyze large amounts of data in a short time and helps organizations make profitable adjustments to operations and production. It can easily predict trends and behaviors and automatically discover hidden patterns.
Data mining can be applied to any business where more or less data is available. We can summarize how large companies and SMEs in different sectors benefit from data mining as follows:
The techniques known as data mining techniques cover all data science techniques, from machine algorithm topics to classification methods.
What is Classification Analysis?
Classification analysis is one of the most basic data mining techniques that helps to classify data. The purpose of classification analysis is to be able to predict behavior or answer an important question. For example, when a credit card company tries to determine which users in its database should receive credit card offers, it analyzes information such as purchase history and annual income and may categorize users as "low risk", "high risk" and "medium risk". Another example of classification analysis is Gmail classifies emails as primary, social, or promotional based on certain key characteristics.
What is Attribution Rule Learning?
Association rules welcomed by market researchers often learn to find interesting relationships between variables in large datasets to reveal events that occur together. For example, a product designer might include this color in a new product line because women in their 40s and 50s love to buy black items. Retailers can also use attribution analysis to find matched products that customers buy together.
What is Regression Analysis?
Regression analysis is used to estimate continuous values based on other variables in the data set. For example, you can use regression analysis to predict the future price of a product based on demand, availability, and other factors. The most commonly used regression techniques are called linear regression and logistic regression.
1-Information About Linear Regression
It allows to estimate the value of the set of unknown variables by providing the analysis of other variables. For example, you can predict the market value of another company by location, industry, or future date of sale, based on data (business type, location, size, sale price, date of sale...) of the most recently sold company.
2-Information About Logistic Regression
The concept of algorithm is very valuable for predicting whether the set of variables fully supports a certain result. For logistic regression to work, the variable must be "binary". In other words, you should investigate how the presence or absence of the variable affects the "yes or no" answer.
What is Clustering?
Clustering technology groups similar and different items together. Clustering; It is the name given to describe the relationship between objects in the unstructured data set in order to provide a structure that is searchable, meaningful and suitable for analysis. For example, you can see that 35% of customers are 25-40 year old men who like to wear navy blue hats. This information can be valuable when targeting new customers in an advertising campaign.
What is Outlier Detection?
Outlier detection is an important issue that supports us to see anomalies in the data. Values defined as abnormal or outliers are values that deviate greatly from the expected state. These situations offer a great use for situations such as fraud detection, network attack monitoring and system performance monitoring, which are frequently encountered today.
What is Time Series Forecast?
Time series forecasting is a machine learning model used to predict the best timing of certain actions. It uses historical data and recognizes patterns in historical data. For example, an automaker might use a time series model to analyze historical data to predict when it needs to be renewed. Similarly, retailers can use this model to plan the launch of new products.
What are Decision Trees?
A decision tree is a modeling technique that relies on a set of binary rules to predict outcomes. The decision tree algorithm uses the same input to produce the same result. Decision trees are known as an important modeling technique used to create regression analysis and classification models.
What are Neural Networks?
The neural network system modeled on the human brain can be very complex at times. This may require companies to hire truly talented employees so they can build and implement neural networks. It is useful in situations that require fast response, such as unmanned vehicle technology.
What is Visualization?
It is touted as a very important field in terms of data mining with visualization systems and is seen as an important tool for generating insights. Most modern data visualization tools use dashboards to quickly organize large data sets. Variable selection is very useful for combining highly correlated variables to detect false information and reduce the size of the dataset. Commonly used data visualization methods include tree diagrams, charts, heat maps, and histograms.
What is Sequential Pattern Mining?
Sequential model mining is known by definition as a system that describes events that occur in a sequential manner. It is mainly applied to transactional datasets and can be used to understand customer behavior. It is a useful and useful system to increase product recommendations and sales opportunities.