Distributed Data Mining Model Research Articles

Predictive Data Mining Model for Electronic Customer Relationship Management Intelligence

The rising adoption of e-CRM strategies in marketing and customer relationship management has necessitated to more needs especially where a specific customer segment is targeted and the services are personalized. This paper presents a distributed data mining model using access-control architecture in a bid to realize the needs for an online CRM that intends to deliver web content to a specific group of customers. This hybrid model utilizes the integration of the mobile agent and client server technologies that could easily be updated from the already existing web platforms. The model allows the management team to derive insights from the operations of the system since it focuses on e-personalization and web intelligence hence presenting a better approach for decision support among organizations. To achieve this, a software approach made of access-control functions, data mining algorithms, customer-profiling capability, dynamic web page creation, and a rule-based system is utilized.

Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks

Wireless sensor network (WSN) comprises a collection of sensor nodes employed to monitor and record the status of the physical environment and organize the gathered data at a central location. This paper presents a deep learning based distributed data mining (DDM) model to achieve energy efficiency and optimal load balancing at the fusion center of WSN. The presented DMM model includes a recurrent neural network (RNN) based long short-term memory (LSTM) called RNN-LSTM, which divides the network into various layers and place them into the sensor nodes. The proposed model reduces the overhead at the fusion center along with a reduction in the number of data transmission. The presented RNN-LSTM model is tested under a wide set of experimentation with varying number of hidden layer nodes and signaling intervals. At the same time, the amount of energy needed to transmit data by RNN-LSTM model is considerably lower than energy needed to transmit actual data. The simulation results indicated that the RNN-LSTM reduces the signaling overhead, average delay and maximizes the overall throughput compared to other methods. It is noted that under the signaling interval of 240 ms, it can be shown that the RNN-LSTM achieves a minimum average delay of 190 ms whereas the OSPF and DNN models shows average delay of 230 ms and 230 ms respectively.

Analysis on Data Mining Model Objected to Internet of Things

Three data mining models suitable to data processing have been proposed according to some technical challenges on the characteristics data for Internet of Things. The first model is the multilayer data mining model, which consists of data collection, data processing, event processing and data mining service. The second model is distributed data mining model that could be used to solve the problem of data stored in different locations. The third model is grid-based data mining model, which could utilize the potentially unlimited amount of data by using grid. At the same time, the paper learns from “decomposition build theory to solve the data mining problem in the internet of things. Finally, the key issues are discussed about model application.

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.