Exploring Bi Data Mining

What is data mining?

• Data mining, also known as knodge DGE discovery, is an automatic or semi-automated method to find potential and valuable information and rules in data.
• Data Mining Technology comes from databases, statistics, and artificial intelligence.

What can Data Mining do?

Analyze a large amount of data generated by an enterprise to find out the hidden rules.
A clearer understanding of the current business operation status
This gives decision makers a scientific basis to grasp the future direction of decision-making.
Predicted sales

• Send emails to specific customers
• Determine products that may need to be launched
• Find the sequence in which the customer puts the product into the shopping cart
• ......

Data Mining Algorithms
Data Mining is a process of extracting knowledge from specific forms of data. Its main task is to describe, classify, and predict data. Common Data prediction techniques for data mining include linear regression, least squares, and neural networks.

Another interesting thing about the analysis service is data mining. In business intelligence, data mining is the highest level. Nowadays, the value of popular big data is often reflected by data mining.

If we say that the Bi process can be seen as the data yesterday, today and tomorrow, and the data yesterday, we can use reports to tell you what happened before the business, the data today, multi-dimensional analysis and other tools tell you why this happened. The data tomorrow is to use data mining algorithms to mine existing massive historical data, so that you can know what your business will look like in the future.

Microsoft's data mining tools include many algorithms, such as Bayesian, decision tree, association rules, and time series analysis.
Data Mining analyzes sample data, discovers rules from them, and then predicts unknown data in the future. It is usually used for e-commerce website product recommendation, potential customer analysis, and customer classification.

Serial number	Data Mining Technology	Description
1	Microsoft Naive Bayes Bayesian Model	The Microsoft Naive Bayes algorithm treats all input attributes as independent and calculates the probability of each pair of INPUT attribute values and predicted attribute values. This algorithm can be used for classification and prediction.
2	Microsoft Association Rules	Microsoft Association Algorithms use correlation statistics between attribute values or transaction items to analyze data.
3	Microsoft Cluster Analysis	The Microsoft Cluster Analysis Algorithm searches for natural groups of data in the multi-dimensional representation of attribute values. This algorithm is useful when you need to discover common groups.
4	Microsoft decision tree	Microsoft decision tree is a classification algorithm suitable for predictive modeling. This algorithm supports prediction of discrete and continuous attributes.
5	Microsoft Logistic Regression	Microsoft logistic regression is a regression algorithm suitable for regression modeling. This algorithm is a Microsoft neural network algorithm obtained by eliminating the hidden layer. This algorithm supports prediction of discrete and continuous attributes.
6	Microsoft Neural Network	Microsoft neural network algorithm
7	Microsoft Time Series	The Microsoft time series algorithm can analyze time-related data to identify various modes based on time series analysis, such as monthly sales and annual profit.
8	Microsoft Sequence Analysis and cluster analysis	Microsoft Sequence Analysis and cluster analysis algorithms combine the other two data mining technologies: Sequence Analysis and cluster analysis. This algorithm analyzes and clusters sequence-related patterns.
9	Microsoft Linear Regression	Microsoft linear regression algorithm is a regression algorithm suitable for regression modeling. This algorithm is a Microsoft decision tree algorithm. It is obtained by disabling splitting (the entire regression formula is placed in a single root node. This algorithm supports prediction of continuous attributes.

Similar to other IT projects, data mining can be divided into the following processes. First, define the problem, then prepare and browse the data, then generate and verify the model, and finally deploy and update the model.

This process is not necessarily the basis of one breath. For example, if no data is found in the model, you need to re-Prepare the data, or, if a problem is found during model verification, you may need to redefine the model.
The query statement used for data mining is DMX, which can be used to create and process a Data Mining Model and make a prediction query.

Exploring Bi Data Mining