Data processing is significant in the era of business competition that requires fast information. Data-related information is helpful in business processes to determine future strategies. That’s why data mining, or what is known as data mining, is essential for the continuity of an ongoing business.
The rapid use of the internet makes data generates vast amounts of data, which is beneficial and can be used in making decisions. In addition, processing using the data. Data mining is a process of collecting and processing data that is carried out using software with the help of statistical calculations.
What Is Data Mining
Data mining is dredging or collecting necessary information from extensive data. Data mining often uses statistical methods and mathematics and artificial intelligence technology.
Alternative names are Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, and others.
If you look at the pictures in the KDD process, many concepts and techniques are used in data mining. The process requires several steps to get the desired data.
Data mining is carried out to fulfill several specific goals. The following are the goals of data mining:
Data mining can be used as a means to explain a research condition.
Data mining is used to confirm a statement or reinforce a hypothesis.
Data mining is used to find new patterns that were previously undetected.
Data Mining Functions
Data mining has many functions. For the main function itself, there are two; Namely, descriptive function and predictive function. Other functions will be discussed below.
The description function in data mining is a function to understand more about the observed data. By carrying out a process, it is expected to be able to find out the behavior of the data. This data can later be used to determine the characteristics of the data in question.
Using the descriptive data mining function, you can find specific patterns hidden in data later. In other words, if the design is repeated and has value, then the characteristics of the data can be known.
The prediction function is how a process finds a specific pattern from data later. These patterns can be identified from the various variables in the data.
When a pattern has been found, the design obtained is used to predict other variables whose value or type is unknown.
That’s why this one function is said to be predictive and does predictive analysis. This function can also be used to predict a particular variable that is not in the data.
So this function makes it easy and profitable for anyone who needs accurate predictions to improve these essential things.
Other data mining functions are characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc.
Multidimensional concept description, characterization, discrimination, or serves to generalize, summarize, and differentiate data characteristics, etc.
Frequent patterns, associations, correlations
Classification and prediction Building models (functions) that describe and differentiate classes or concepts for future predictions. For example, Classify countries by (climate), or classify cars by (gas mileage)
Cluster analysis, Grouping data to form a new class. For example, Maximize intra-class similarity & minimize inter-class similarity
Outlier analysis, Data objects that do not fit the general behavior of data, Useful in fraud detection and rare event analysis.
Trend and evolution analysis, Trend and deviation: eg
Regression analysis or Mining Sequential pattern mining: eg
Digital camera, or Periodicity analysis and Similarity-based analysis.
Other pattern-directed or statistical analyses
Data Mining Methods
There are several methods used to perform data mining. The following is the method:
The first technique is association. Association is a rule-based method used to find associations and relationships of variables in a data set.
Usually, this analysis consists of simple “if or then” statements. Association is widely used in identifying product correlations in shopping carts to understand consumer consumption habits.
Thus, companies can develop sales strategies and make better recommendation systems.
Furthermore, classification is the most commonly used method in data mining. Classification is an action to predict the class of an object.
Regression is a technique that explains dependent variable through independent variable analysis. For example, predicting sales of a product based on the correlation between product prices and the average income level of customers.
Clustering is used in dividing the data set into several groups based on the attributes’ similarity. An example of the case is Customer Segmentation. He divides customers into several groups based on the degree of similarity.
Implement Data Mining
1. Market Analysis
The function of data mining in the marketing sector is market analysis, market segmentation (STP), market research, market targeting, cross-selling, and customer relationship management, or CRM. There are several examples of data mining applications in the marketing sector.
Target marketing to find groups or customers with characteristics similar to their needs. Based on behavior, interests, income levels, and so on that represent the characteristics of consumers in the market.
- Market traffic analysis aims to find the relationship between product sales and predictions based on associations.
- Consumer profiling determines what kind of consumers need services or goods, which will later be classified according to their roles.
- Consumer needs are analyzed to identify the best product for several customer groups. They continue to market orientation, estimate factors that can attract sales leads, and analyze consumer information.
2. Corporate Analysis
In the corporate sector, data mining plays a vital role in customer retention, which is called customer retention, competitor analysis to quality control. However, here are some examples of data mining applications for corporate analysis.
Financial planning, analysis process from cash flow and financial forecasting, and cross-sectional and time series analysis to business asset evaluation.
Resource planning to summarize and compare resources to expenses in production.
Competition is carried out by competitive analysis and competitor analysis strategies, setting strategies for setting market prices to be competitive by setting prices and class basis procedures.
3. Fraud Detection & Mining Unusual Patterns
Data mining also functions to find and detect fraud in a system. By using mini data, you can see the millions of incoming transactions.
Approach: Clustering & model construction for fraud, outlier analysis
Applications: Healthcare, retail, credit card services, Telecomm. For example, auto insurance, money laundering, health insurance, telecommunications, analysis of patterns that deviate from the expected norm, retail industry, etc.