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Weighted K-Means for Density-Biased Clustering
TLDR
We propose an algorithm on density biased sampling based on the reservoir technique and a weighted k-means algorithm to cluster a data sample augmented with weights. Expand
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An Empirical Study of Distance Metrics for k-Nearest Neighbor Algorithm
This research aims at studying the performance of k-nearest neighbor classification when applying different distance measurements. In this work, we comparatively study 11 distance metrics includingExpand
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Time Series Analysis of Household Electric Consumption with ARIMA and ARMA Models
—The purposes of this research are to find a model to forecast the electricity consumption in a household and to find the most suitable forecasting period whether it should be in daily, weekly,Expand
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Decision Rule Induction in a Learning Content Management System
TLDR
We propose a knowledge induction technique to support course developers in designing flow of content in the learning content management system. Expand
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The Clustering Validity with Silhouette and Sum of Squared Errors
The data clustering with automatic program such as k-means has been a popular technique widely used in many general applications. Two interesting sub-activity of clustering process are studied inExpand
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Feature Selection and Boosting Techniques to Improve Fault Detection Accuracy in the Semiconductor Manufacturing Process
TLDR
We propose the techniques based on the data mining technology to automatically generate an accurate model to predict faults during the wafer fabrication process of the semiconductor industries. Expand
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Bridging Data Mining Model to the Automated Knowledge Base of Biomedical Informatics
TLDR
This paper illustrates the knowledge deployment step in which its input is the induced knowledge, in the formalism of classification rules. Expand
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Database Reverse Engineering based on Association Rule Mining
TLDR
In this paper, we propose a method to discover conceptual schema from the database instances, or relations. Expand
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A lightweight method to parallel k-means clustering
TLDR
We propose the parallel method as well as its approximation scheme to the k-means clustering. Expand
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