# Marketing Decision Support System Based on Data Mining Technology
## 3 Method  
(1) The concept and model of DM  
DM is a process of mining interesting knowledge from a large amount of data stored in databases, data warehouses, or other information bases. To put it more popularly, we extract the parts we need from a lot of data and ignore the useless parts. DM is a good method of knowledge extraction [25]. DM enables valuable knowledge, rules, or high-level information to be extracted from the relevant data sets of the database, making the large-scale database serve as a rich and reliable resource for decision-making. The core technologies of DM are artificial intelligence, machine learning, statistics, etc. However, a DM system is not a simple combination of multiple technologies, but a complete whole. It also needs the support of other auxiliary technologies. The main methods and technologies used include statistics, cluster analysis and pattern recognition, decision tree classification, artificial neural network and genetic algorithm, rule induction, and visualization technology. The typical knowledge pattern extraction methods of data mining include classification, concept description deviation detection, and sequential pattern and association rule discovery. It can complete a series of tasks such as data collection, preprocessing, data analysis, and result expression, and finally present the analysis results to users [26]. The data extraction layer processes the original data and obtains some valuable data. On this basis, the information extraction layer uses a variety of data analysis tools to extract useful information for decision-making from the data. OLAP and DM are powerful tools for information extraction. The purpose of establishing a data warehouse is to provide the OLAP system with processed data. OLAP is characterized by inquiry and analysis and provides real-time and accurate information according to the decision-maker's demand for information. OLAP can quickly, consistently, and interactively access various possible information views, providing a favorable means for decision support. The key problem in the implementation of OLAP is how to organize the comprehensive data in the data warehouse to meet the needs of multi-dimensional data analysis of client users. At present, there are mainly two organization modes: multi-dimensional OLAP (MDOLAP) and relational OLAP (ROLAP). MDOLAP takes multi-dimensional database as the core and displays data in a multi-dimensional way. It can intuitively express the \"one to many\" and \"many to many\" relationships in the real world, and its multi-dimensional concepts are clearly expressed. ROLAP's comprehensive data organization mode takes relational database as the core and uses two-dimensional tables in relational database to organize data and express multidimensional concepts. Its data organization adopts star pattern. The ROLAP-oriented data organization pattern with the sales analysis in marketing analysis as the theme is shown in Figure 1.  
Each topic in the data warehouse corresponds to a star schema structure, which is composed of fact tables and several dimension tables. Each record in the fact table contains a pointer to each dimension table, through which multidimensional data can be linked. The data warehouse design of the system adopts star pattern. Therefore, the OLAP design of the system adopts ROLAP mode [27]. The classification analysis of the decision tree is mainly to train the decision tree or Bayesian neural network through the transaction samples in the transaction database to form the classification rules for the sample points in the sample. It can explain the main and secondary attributes that form classification differences in the samples and can classify and predict the samples with set attributes according to the classification rules in the decision tree. The system uses ID3 algorithm, a representative algorithm based on information gain theory, to realize decision tree analysis. Decision tree analysis can obtain the levels and categories of various influencing factors and the resulting sales level.