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In recent years, data mining has become an important process for enterprises and small and medium-sized enterprises (SMEs). Data mining helps turn the received data into an understandable basis for further use. But what is data mining and data warehousing?
In this blog, we will discuss data mining and its various benefits in the business world. We will also discuss the differences between a data warehouse and data mining , and how a data warehouse can benefit a business .
What is data mining?
Data mining is not only popular in the business world, but is often applied to large-scale data or information processing, as well as computer decision support system applications. Simply put, it involves the process of extracting and finding patterns in large data sets using methods at the interface of machine learning, statistics, and database systems. More precisely, it is a sub-field between the disciplines of computer science and statistics, and its overall goal is to extract information from data sets and transform the information into understandable structures for later use.
Data mining is a key component of data analysis. It is a major branch of data science that uses advanced analytical techniques to find useful information in data sets. Interestingly, data mining has its drawbacks. This is because the goal is to extract patterns and insights from large amounts of data, not data mining.
Simply put, data mining is the process of sequencing large data sets to find patterns and relationships that can help solve business problems through data mining . Thanks to the tools and methods used, companies can predict future trends and make more accurate business decisions.
What is the importance of data mining?
On a more specific level, data mining is a core part of every organization's analytics strategy. The resulting data can be used to further analyze historical data in business intelligence and advanced analytics applications. It can also be used for real-time analytics applications that analyze streaming data while generating and collecting it.
At the same time, data mining can help in many aspects of enterprise strategy development and management. For example, marketing, advertising, sales, customer service, supply chain management, finance and others. In addition, data mining supports other security-related aspects of an organization such as fraud detection, risk management, cybersecurity planning, and more. It is also important in health care, government, research, sports, mathematics, and many other fields.
How does data mining work?
Typically, data scientists are responsible for data mining. However, employees working in organizations, such as skilled business and data scientists, data-savvy business analysts, executives, and citizen data scientists, can also undertake this process.
The key components of data mining are machine learning, statistical analysis, and data management tasks to prepare data for analysis. Overall, the integration of machine learning algorithms and artificial intelligence tools further automates the process and makes it easier to analyze large data sets such as customer databases, transaction logs, web server log files, etc.
The data mining process can be divided into 4 main stages: data collection; data preparation; data collection; Data Analysis and Interpretation .
1. Data collection . This process includes identifying and gathering relevant data for analytical applications. While data may reside in multiple source systems such as data warehouses or data lakes, external data sources can also be used. However, regardless of the original data source, the data scientist can access the data lake for the remainder of the process.
2. Data preparation . The following process performs several steps before data mining. In addition, the first step is data exploration, profiling, and preprocessing. Lastly, process through data cleansing to eliminate errors and data quality issues.
3. Data mining . After preparing the data, the data scientist selects the appropriate data mining method and applies one or more algorithms to start the data mining process. In machine learning applications, the algorithm must (usually) be trained on a sample data set. This is done to find the information you are looking for before running with the entire data set.
4. Data analysis and interpretation . After receiving the results of data analysis, it is used to build analytical models for decision making, including other business actions. Data processors or other members of the data processing team must communicate the results to business managers and users. However, this is often done through data visualization and data storytelling techniques.
What are the different data mining methods?
Basically, different data mining techniques are used for different scientific data applications. However, a common use of data mining is pattern recognition, which is provided by various methods. Also, another common application is anomaly detection, which aims to detect outliers in a data set. However, the popular types of data mining methods are:
1. Extraction of association rules . First, association rules are if statements that define the relationship between data elements in data mining. Relational access, support, and trust criteria were used to evaluate the relationship; Support measures how often a related item appears in the data set, while trust measures how often a statement is true.
2. Classification . Second, this method categorizes dataset elements into different categories determined by the data mining process. Examples of classification methods include: decision trees, naive Bayes classifiers, k-nearest neighbors, and logistic regression.
3. Grouping . Third, this process groups data items with certain characteristics into data mining applications. Some examples are k-means clustering, hierarchical clustering, and mixed Gaussian models.
4. Regression Then regression is another way to look for relationships in a data set by calculating the predicted data values based on a set of variables. In fact, decision trees and other classification methods can also be used to perform regression.
5. Sequence and path analysis . In some cases, data mining can also be performed to identify patterns in subsequent events or a specific set of values.
6. Neural network . A neural network is a set of algorithms that mimic the activity of the human brain. Neural networks are especially useful in complex pattern recognition applications involving deep learning, a more advanced branch of machine learning.
What are the characteristics of data mining?
Data mining analysis must be carried out using the analysis focus feature. However, this property can be the only property of the focus element. Sometimes they can function at a higher level than the level of the focus element.
However, profiling functions of varying complexity can be used to capture the focal features of the analysis that should be included in data mining analysis. Basically, each object forms a column in the results table, and different types of objects according to different ways to change the input model to calculate the required characteristics of the analysis focus.
1. The focus attribute. Attributes that depend on a single focus element, such as store or day, are easiest because the value is an expression of the value in the source database table.
2. Aggregation: usually the result of combining many functions. Since the degree of individual processing is difficult to predict, its properties must be combined into a meaningful level of attention. However, in the general case, the consolidation process occurs at all levels of focus.
3. Cumulative Distribution : When analyzing stores (especially sales performance), it is customary to include a portion of sales from the relevant departments in the analysis. But this can easily be done using the number divided. However, the daily sales volume of the store is divided by the total sales volume of each department.
4. Disaggregation. Some data mining algorithms require categorical input rather than numerical input. In such a case, the data must be preprocessed so that the values in some numeric range are mapped to discrete values.
5. Comparison of values. It's important to note that value matching is similar to numeric feature isolation, where users can assign new values to individual feature values.
6. Calculation. To evaluate a function from another function, you can execute any SQL statement. The calculation process is simple and involves adding or dividing two functions, or it can be more complex depending on the problem.
What are the benefits of data mining?
Here are some of the benefits of data mining:
- Marketing and/or retail
Interestingly, data mining helps marketers navigate by providing useful and accurate trends in users' buying behavior. Marketers can target customers based on these trends. Data mining can also help marketers predict which products customers will prefer. This can help create an interactive and enjoyable shopping experience for customers. Apart from the marketing department, retail stores also benefit from data mining.
- Bank and/or loan
Data mining is useful for financial institutions, especially in terms of loan documentation and credit history. This process helps credit card issuers detect fraudulent credit card transactions. While this method is not completely accurate in predicting fraudulent payments, data mining can help credit card issuers reduce their losses.
- law enforcement
The data mining process helps law enforcement identify and eliminate criminal suspects by identifying suspicious locations, crime patterns, habits, and other behaviors.
- Researcher
In addition, the data mining process also helps researchers. This allows them to accelerate the data analysis phase; This gives them more time to work on different projects.
Often people confuse data warehousing and data mining as similar processes. Although they are both data management and maintenance processes, there are significant differences between the two. Therefore, let's take a quick look at a data warehouse and how it differs from data mining.
What is data storage?
Data warehouse is a method of collecting and managing data from various sources to support important business concepts. It is the combination of technologies and components that enables the strategic use of data. In other words, a data warehouse is an electronic repository of large amounts of enterprise information designed for retrieval and analysis, not transaction processing. Basically , it is the process of converting data into information and providing it to the user for analysis.
In 1990, Bill Inman coined the term "data warehouse". According to him, a data warehouse is a domain-specific, integrated, time-varying, and non-volatile collection of data that helps analysts make informed decisions within organizations. In addition, data warehouses provide aggregated and aggregated information in a multidimensional manner. It provides online analytical processing (OLAP) tools, interactive and efficient data analysis in multidimensional space. This analysis also includes data summarization and data mining.
What are the characteristics of a data warehouse?
Following are the main features of the data warehouse:
- Domain-Specific : To begin with, a data warehouse is domain-specific because it provides information about the subject rather than the current organizational processes. , , ,
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аведамленне о акое ектуальны аналіз адзеных овішча адзеных? ершыню а 'явілася а Tech Research Online.