We are inundated with data but lack knowledge

Solution: Data Warehousing and Data Mining

Data collection, database creation, IMS and mesh DBMS

Relational database model, relational DBMS implementation

RDBMS, advanced data models (extended relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.)

Data mining and data warehousing, multimedia databases, and web databases

1. The first and most important requirement before starting knowledge discovery is to understand the data and business problem. You must have a clear definition of your goals, that is, decide what you want to do

Building a data mining library includes the following steps: data collection, data description, selection, data quality assessment and data cleaning, merging and integration, building metadata, loading data mining library, and maintaining data mining library

The purpose of the analysis is to find the data fields that have the greatest impact on the forecast output and determine whether export fields need to be defined.

This is the last step of data preparation before building the model. This step can be divided into four parts: select variables, select records, create new variables, and convert variables

Building a model is an iterative process. Different models need to be carefully examined to determine which model is most useful for the business problem faced. First use a portion of the data to build a model, and then use the remaining data to test and validate the resulting model.

After the model is established, the results obtained must be evaluated and the value of the model explained. The accuracy obtained from the test set is only meaningful for the data used to build the model

Once a model is built and validated, it can be used in two main ways. The first is to provide analysts with a reference; the other is to apply this model to different data sets.

It first selects a training set that has been classified from the data, uses data mining technology on the training set to build a classification model, and then uses the model to classify unclassified data.

Valuation is similar to classification, but the final output result of valuation is a continuous value, and the amount of valuation is not predetermined. Valuation can serve as preparation for classification

It is performed through classification or valuation, and a model is obtained through classification or valuation training. If the model has a high accuracy for the test sample group, the model can be used for unknown variables in new samples. make predictions

The aim is to discover which things always happen together

It is a method of automatically finding and establishing grouping rules. It divides similar samples into a cluster by judging the similarity between samples.

Where are the data sources used for analysis?

Target marketing

Determine customer buying patterns over time

Cross-market analysis

Customer profiling

Identify customer needs

Provide summary information

Financial Planning and Asset Valuation

resource planning

compete

application

method

example

sports

astronomical

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