Data mining is the process of knowledge discovery in the database. The basic need of data mining in business is to help in improving the business. There are different types of businesses based on domains. Data mining has been recognized as an essential element of decision support, which has increasingly become a focus of the database industry. Real-world data mining generally must consider and involve domain and business oriented factors such as human knowledge, constraints and business expectations to discover the knowledge more accurately and correctly. This domain related data can play an important role in data mining algorithms to make it a domain driven knowledge discovery. In this paper an open source multi agent framework is proposed for domain driven data mining. Various open sources API's (Application Programming Interfaces) available are also discussed for different kind of logical operations in data pre-processing, and an open source multi agent system is proposed.
Keywords: Domain driven data mining, knowledge discovery ,domain expert, application programming interface, software agent, multi agent system.
[...] The concept of software agent is applied over domain driven data mining and a multi agent system (MAS) for domain driven data mining (DDDM) is proposed. The software agent based development and design methodology are explained in and A typical DDDM framework is also explained in In this paper we have suggested a multi agent system for a domain driven data mining applications. First of all the DDDM process is explained then proposed multi agent system is explained with modules layers for a domain driven data mining applications and suggested some frequently used common open source Application Programming Interfaces (API's) for each and every module Software Agent An agent is an autonomous software unit that can exist independently of other similar units in the software system. [...]
[...] A few examples of open source Application Programming Interfaces (API's) for all the layers in table- MAS for DDDM The concepts of software agents are applied for various domain driven data mining activities and an open source multi agent system is proposed for domain driven data mining. Data is fetched and saved back in data access layer. Database access layer can be implemented in JDBC or in Hibernate etc. Various software agents for proposed multi agent system is mentioned in table 1 with the corresponding open source application programming interface (API). [...]
[...] Layer 6 Layer 5 Layer 4 Layer 3 Layer 2 Layer 1 Layer 0 Fig N-tier framework for DDDM There are eight software agents working individually for domain driven data mining application based on the architecture depicted in fig The first agent is Data Access Agent, the responsibility of this agent is to access the data from the database and take care of update, insert and delete operations. The second software agent is Data Preprocessing Agent, responsibilities of this agent is to replace the synonyms, handles the missing values and performance data clean operations. [...]
[...] Proposed Multi Agent System for DDDM In this section the proposed multi agent system for domain driven data mining is explained. First the general architecture of a domain driven data mining is explained then how a multi agent system can be built on this architecture is presented. At last available open source application programming interfaces (API's) for mentioned agents are given A typical domain driven data mining architecture Figure 3 depicts a typical architecture for domain driven data mining application. [...]
[...] It supports many output types Conclusion and Future Directions In this paper a multi agent system for domain driven data mining is proposed and suggested number of open source API's to implement the proposal. The future direction could be implementing an agent oriented generic framework for DDDM and profiling related tasks can also be integrated to capture the performance related data References Margus Oja, Boris Tamm and Kuldar Taveter: “Agent-Based Software Design”, Proc. Estonian Acad. Sci. Eng Hillol Kargupta, Ilker Hamzaoglu, Brian Stafford: “Scalable, Distributed Data Mining Using an Agent Based Architecture”, Proceedings of High Performance Computing Cao, L., Zhang, C., Yu, P., et al. [...]
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