Cost Models for Distributed Data Mining
نویسندگان
چکیده
The objective of this paper is to present cost models for estimating the response time for the distributed data mining (DDM) process. These cost models form the basis for developing a hybrid approach to distributed data mining which integrates the client-server and mobile agent paradigms. The underlying objective of the hybrid model is to optimise the DDM process.
منابع مشابه
Entropy-based Consensus for Distributed Data Clustering
The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...
متن کاملA New Cost Model for Estimation of Open Pit Copper Mine Capital Expenditure
One of the most important issues in all stages of mining study is capital cost estimation. Determination of capital expenditure is a challenging issue for mine designers. In recent decade, quite a few number of studies have focused on proposing estimation models to predict mining capital cost. However, these efforts have not achieved to a predictor model with reliable range of error. Both of ov...
متن کاملPrediction of Blasting Cost in Limestone Mines Using Gene Expression Programming Model and Artificial Neural Networks
The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone mines in Iran using the artificial neural networks (ANNs), gene expression programming (G...
متن کاملReducing Communication Cost in a Privacy Preserving Distributed Association Rule Mining
Data mining is a process that analyzes voluminous digital data in order to discover hidden but useful patterns from digital data. However, discovery of such hidden patterns has statistical meaning and may often disclose some sensitive information. As a result privacy becomes one of the prime concerns in data mining research community. Since distributed association mining discovers global associ...
متن کاملA Fully Distributed Framework for Cost-Sensitive Data Mining
Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach is to apply machine learning algorithms to compute descriptive models or classifiers from the available data. Two of the main challenges in this area are that i) databases are large and possibly physically distributed, and ii) data are cost-sensitive, or exampl...
متن کامل