data mining is relatively successful in the commercial field, especially in the retail industry. Due to the widespread use of various business systems, coupled with the visual analysis of business intelligence BI, enterprises can collect a large amount of data on purchases, and the amount of data is constantly increasing. Using data mining technology can provide business management personnel with the correct decision-making methods, which is helpful in promoting sales and improving competitiveness.
What is data mining
has different positions and different definitions of data mining.
Data Mining is a process of extracting hidden information and knowledge that people do not know in advance but potentially useful from a large amount of incomplete, noisy, fuzzy, and random practical application data.
. Definition from a business perspective
Data mining is a new commercial information processing technology, its main feature is to extract, transform, analyze and other modeling processing of a large amount of business data in a commercial database, and extract key data to assist in business decision-making.
Therefore, data mining can be described as an advanced and effective method that explores and analyzes a large amount of enterprise data according to the established business goals of the enterprise, reveals hidden, unknown or verifies known regularity, and further models it.
classification of data mining
data mining is divided into guided data mining and unguided data mining. Guided data mining is to build a model using available data, which is a description of a specific attribute. Unguided data mining is about finding a relationship among all attributes. Specifically, classification, valuation, and prediction belong to guided data mining; association rules and clustering belong to unguided data mining.
Category
It first selects the training set that has been classified from the data, uses data mining technology on the training set to establish a classification model, and then uses the model to classify data without classification.
Value
Value 1Value is similar to classification, but the final output of the valuation is a continuous value, and the amount of the valuation is not predetermined. Valuation can be used as a preparation for classification.
prediction
It is carried out through classification or valuation. A model is derived through training of classification or valuation. If the model has a high accuracy rate for the test sample group, the model can be used to predict unknown variables of the new sample.
association
association The purpose of the association is to find that some things always happen together.
clustering
It is a method to automatically find and establish grouping rules. It divides similar samples into a cluster by judging the similarity between samples.
The difference between data analysis and data mining
data analysis, is a process of analyzing the massive data collected using appropriate statistical methods, extracting useful information and forming conclusions, and then studying and summarizing the data in detail.
data mining, is a complex process of mining valuable (unknown and regular) information from massive data through corresponding algorithms.
Data mining is a deep-level data analysis, and data analysis is a shallow-level data mining. Data mining focuses more on exploratory data analysis, because the focus of data mining is to discover knowledge laws from data.
Application field
Search engine: Data mining technology is applied to the search engine field, thereby generating intelligent search engine , which will provide users with an efficient and accurate retrieval tool.
Financial field: Data mining can be used to analyze customer reputation. Typical financial analysis areas include investment assessment and stock trading market forecasting.
data mining can also be used in industry, agriculture, transportation, telecommunications , military, Internet and other industries. Data mining has a wide range of application prospects, and it can be used in decision support and database management systems.
With the rapid growth of digital economy in recent years, new terms about numbers and data have become hot words around the world. If enterprises want to enhance their competitiveness in the digital age, they must make good use of their data assets. On the one hand, they are reforming and developing themselves, and on the other hand, they also lead other enterprises to transform into .
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