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Many methods have been proposed to measure the similarity between time series data sets, each with advantages and weaknesses. It is to choose the most appropriate similarity measure depending on the intended application domain and data considered. The performance of machine learning algorithms depends on the metric used to compare two objects. For time series, Dynamic Time Warping (DTW) is the most...
The purpose of Dynamic Time Warping (DTW) is to determine the shortest warp path, corresponding to the optimal alignment between two sequences. It is one of the most used methods for time series distance measure. DTW was introduced to the community as a Data Mining utility for various tasks for time series problems such as classification and clustering. Many variants of DTW aim to accelerate the calculation...
Measuring similarity or distance between two data points is fundamental to many Machine Learning algorithms such as K-Nearest-Neighbor, Clustering etc. Depending on the nature of the data point, various measurements can be used. DTW is largely used for mining time series but it is not adopted to large data sets because of its quadratic complexity. Global constraints narrow the search path in the matrix...
This paper presents a binarization pre-processing strategy for mixed datasets. We propose that the use of binary attributes for representing nominal and integer data is beneficial for classification accuracy. We also describe a procedure to convert integer and nominal data into binary attributes. Expectation-Maximization (EM) clustering algorithms was applied to classify the values of the attributes...
The hazardous waste management is composed of three components: the allocation of the different hazardous waste generators, the hazardous waste routing and the hazardous waste location problems. In this paper, we focus on the allocation problem. It is very important to perform this task since it affects the location and the routing problems. Minimizing the risk and maximizing the equity distribution...
Scheduling and human resources management have an important role in the productivity and competitiveness of a company. Timetabling is an essential task in such filed to compete in many sectors. The conventional method which is traditionally done manually for solving timetabling problem is often inaccurate. This paper move toward automated software to solve the timetabling problem for the SORETRAS...
Nowadays, classification is one of the many fields in Data Mining, also known as Knowledge Discovery in Databases, which aims at extracting information from large data volumes. In order to achieve this, data mining uses different computational techniques from machine learning, statistics and pattern recognition. In this work, a Data Mining techniques is used to help the Decision Maker of a Marin Transportation...
In this paper we propose a new procedure for classification based on a hybrid approach. The classification problem is solved by minimizing the distance between the components of each clusters and the centers of the clusters. The determination of the cluster centers is therefore a critical step in our approach and was addressed used the k-means algorithm. Once the centers of each class are determined,...
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