Distributed Data Mining Systems Research Articles

Distributed data mining systems: techniques, approaches and algorithms

Nowadays, we are living in the midst of a data explosion and seeing a massive growth in databases so with the wide availability of huge amounts of data; necessarily we are become in need for turning this data into useful information and knowledge, where Data mining uncovers interesting patterns and relationships hidden in a large volume of raw data and big data is a new term used to identify the datasets that are of large size and have grater complexity. The knowledge gained from data can be used for applications such as market analysis, customer retention and production control. Data mining is a massive computing task that deals with huge amount of stored data in a centralized or distributed system to extract useful information or knowledge. In this paper, we will discuss Distributed Data Mining systems, approaches, Techniques and algorithms to deal with distributed data to discover knowledge from distributed data in an effective and efficient way.

Research of Distributed Data Mining System Based on Web Services

For the following reasons, the original centralized data mining became more and more out of date. First, the data source need to be processed is distributed on different computers in the networks. Second, because of the constrain of networks band, the privacy and safety of data, the incompatibility of systems and so on, it is not realistic to put all data source in a place for centralized data mining. Third, more and more demands have addressed on the openness and easy accessibility. The distributed data mining technology was presented for the problems mentioned above. This article introduced the latest technology for distributed component technology—web services technology into distributed data mining field, and took some tentative efforts in solving the problem of designing for suitable architecture of distributed data mining systems and corresponding distributed mining algorithms.

Distributed data mining for e-business

In the internet-based e-business environment, most business data are distributed, heterogeneous and private. To achieve true business intelligence, mining large amounts of distributed data is necessary. Through a thorough literature review, this paper identifies four main issues in distributed data mining (DDM) systems for e-business and classifies modern DDM systems into three classes with representative samples. To address these identified issues, this paper proposes a novel DDM model named DRHPDM (Data source Relevance-based Hierarchical Parallel Distributed data mining Model). In addition, to improve the quality of the final result, the data sources are divided into a centralized mining layer and a distributed mining layer, according to their relevance. To improve the openness, cross-platform ability, and intelligence of the DDM system, web service and multi-agent technologies are adopted. The feasibility of DRHPDM was verified by building a prototype system and applying it to a web usage mining scenario.

Grid-Based Platform for Mining Association Rules

distributed data mining is widely used in industrial and commercial applications to analyze large datasets maintained over geographically distributed sites. This paper discusses the disadvantages of existing distributed data mining systems, and puts forward a distributed data mining platform based grid computing. The experiments done on a data set showed that the proposed approach produces meaningful results and has reasonable efficiency and effectiveness providing a trade-off between runtime and rule interestingness.

The Distributed Data-Mining Worksho

The Workshop on Distributed Data Mining (DDM), held at the Fourth International Conference on Knowledge Discovery and Data Mining, brought interested researchers and practitioners together and created an environment for crystallizing the fast-growing field of DDM. The workshop focused on the state-of-the-art DDM algorithms, systems, and application-related issues.