QUEST is a multi-task data mining system developed by IBM Almaden Research Center, which aims to provide efficient data mining basic components for the application and development of a new generation of decision support systems. The system has the following characteristics:

It provides various mining functions for large databases: association rule discovery, sequence pattern discovery, time series clustering, decision tree classification, incremental active mining and so on.

Various mining algorithms have approximate linear (O(n)) computational complexity, and can be applied to databases of any size.

The algorithm is complete, that is, it can find all patterns that meet the specified type.

According to different discovery functions, corresponding parallel algorithms are designed.

2.MineSet

MineSet is a multi-task data mining system jointly developed by SGI and Stanford University. MineSet integrates a variety of data mining algorithms and visualization tools to help users explore and understand the knowledge behind a large amount of data intuitively and in real time. MineSet has the following characteristics:

MineSet is famous for its advanced visual display.

Provide a variety of options, such as: choose J, vote for J, choose J, choose J, choose J, choose J, choose J, choose J, choose J, choose J? br & gt

Support multiple relational databases. You can read data directly from tables in Oracle, Informix and Sybase, or you can execute queries through SQL commands.

A variety of data conversion functions. Before mining, MineSet can delete unnecessary data items, count, aggregate and group data, convert data types, construct expressions to generate new data items from existing data items, and sample data.

The operation is simple, supports international characters, and can be directly published on the Web.

3.DBMiner

DBMiner is a multi-task data mining system developed by SimonFraser University in Canada, and its predecessor is DBLearn. The purpose of system design is to integrate relational database and data mining, and discover all kinds of knowledge based on attribute-oriented multilevel concepts. The DBMiner system has the following characteristics:

It can discover a variety of knowledge: generalization rules, feature rules, association rules, classification rules, evolutionary knowledge, deviation knowledge and so on.

A variety of data mining technologies are integrated: attribute-oriented induction, statistical analysis, gradual deepening of multi-level rules, meta-rule guided discovery and so on.

An interactive SQL-like language-data mining query language DMQL is proposed.

It can be smoothly integrated with relational database.

Unix and PC(Windows/NT) versions of the system based on client/server architecture are realized.