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Supply chain management aims at delivering goods in the shortest time at the lowest possible price while ensuring the best possible quality and is now vital to the success of the online retail business. Executing effective warehouse site selection has been one of the key challenges in the development of a successful supply chain system. While some effective strategies for warehouse site selection...
We propose EC3, a novel algorithm that merges classification and clustering together in order to support both binary and multi-class classification. EC3 is based on a principled combination of multiple classification and multiple clustering methods using a convex optimization function. We additionally propose iEC3, a variant of EC3 that handles imbalanced training data. We perform an extensive experimental...
Generative models are used in an increasing number of applications that rely on large amounts of contextually rich information about individuals. Owing to possible privacy violations, however, publishing or sharing generative models is not always viable. In this paper, we introduce a novel solution for privately releasing generative models and entire high-dimensional datasets produced by these models...
In traditional text sentiment analysis methods, text feature vector has the problem of high dimensionality and high sparseness. In view of this situation, we can cluster the similar words together and use the generated clusters to fit into a new dimension so that the text feature vector dimension will be decreased. By using Word2Vec tool and K-means clustering algorithm, this task can be completed...
A promising direction in deep learning research is to learn representations and simultaneously discover cluster structure in unlabeled data by optimizing a discriminative loss function. Contrary to supervised deep learning, this line of research is in its infancy and the design and optimization of a suitable loss function with the aim of training deep neural networks for clustering is still an open...
This paper develops a hybrid electricity price-forecasting framework to improve the accuracy of prediction. A novel clustering method is proposed that uses a modified game theoretic self-organizing map (GTSOM) and neural gas (NG) along with competitive Hebbian Learning (CHL) to provide a better vector quantization (VQ). To resolve the deficiency of the original SOM, five strategies are proposed to...
The numerical value discretization is an important task of the data preprocessing phase within the intelligent data analysis. This process allows us to reduce the number of values (among other advantages) with which techniques work, reducing the computational cost when it comes to working with large amounts of data. In this paper a numerical value discretization technique is proposed. Specifically,...
In this paper, we present a novel spectrum mapping method — Continuous Frequency Warping and Magnitude Scaling (CFWMS) for voice conversion under the Joint Density Gaussian Mixture Model (JDGMM) framework. JDGMM is a mature clustering technique that models the joint probability density of speech signals from paired speakers. The conventional JDGMM-based approaches morph the spectral features via least...
The segmentation of some scenes can be better for its next step in the processing and analysis. In this paper, the Gaussian mixture model clustering is used to detect and segment the scenes in the sports competition. Firstly, the main color of the scene is extracted by the method of color space histogram, and it is used as a sample for local training. Then we use the expectation maximization algorithm...
In this work we derive a novel clustering scheme for hyperspectral pixels according to the material they sense. We utilize statistical correlations that pixels sensing the same material exhibit. Specifically, kernel learning is combined with a norm-one regularized canonical correlations framework that can perform data clustering on nonlinearly dependent data. To tackle the derived minimization formulation...
In this paper, a novel iterative clustering based active learning (ICAL) method for hyperspectral image classification is proposed. On the one hand, the extreme learning machine is combined with the Markov random field (ELM-MRF) for label assignment, to exploit both spectral and spatial information to boost classification result. On the other hand, an iterative clustering based sample selection strategy...
This paper investigates the continued need for intrusion detection systems (IDS) in computer networks. It explores some of the ways that data mining techniques can be used to improve IDS, and looks at how others have implemented those techniques. It then highlights a method for developing an intrusion detection model using DBSCAN clustering and presents the results of the clustering algorithm as applied...
The background of this paper is the issue of how to overview the knowledge of a given query keyword. Especially, we focus on concerns of those who search for Web pages with a given query keyword. The Web search information needs of a given query keyword is collected through search engine suggests. Given a query keyword, we collect up to around 1,000 suggests, while many of them are redundant. We cluster...
Generating emotional body expressions for socially assistive robots has been gaining increased attention to enhance the engagement and empathy in human-robot interaction. In this paper, we propose a new model of emotional body expression for the robot inspired by social and emotional development of infant from their parents. An infant is often influenced by social referencing, meaning that they perceive...
Learning Management System, such as Moodle, has been utilized extensively as part of e-learning implementation for higher institutions. The flexibility of LMS to convey the learning materials in many ways and approaches enable the instructor to implement blended learning. The student's interaction and activities while learning are captured by Moodle in the log data file and are useful to identify...
As a kind of popular problem in machine learning, multi-instance task has been researched by means of many classical methods, such as kNN, SVM, etc. For kNN classification, its performance on traditional task can be boosted by metric learning, which seeks for a data-dependent metric to make similar examples closer and separate dissimilar examples by a margin. It is a challenge to define distance between...
One of the most used neural network model for clustering data is the Self-Organizing Map (SOM). Over the years, it has been applied in many areas, from computing to biology, and therefore a wide range of data types have been considered. Originally, the SOM was developed to take real-valued data into account. Thus, learning other data types, such as binary and category data, remains a challenge. This...
In this study, we propose an ensemble learning architecture called "Cognitive Learner", for classification of cognitive states from functional magnetic resonance imaging (fMRI). Proposed architecture consists of a two-layer hierarchy. In the first layer, called voxel layer, we model the connectivity among the voxel time series to represent the detailed information about the experiment. In...
The Nearest Neighbor Classification (NNC) has been widely used as classification method, due to its simplicity, classification efficiency and its ability to deal with different classification problems. Despite its good classification accuracy, the NNC suffers from many shortcomings on the execution time, noise sensitivity, high storage requirements and lack of interpretability. In this paper, we propose...
In this paper we present a methodology for monitoring of human activities in home using audio recordings captured from mobile phone. Specifically, after estimating a large set of audio features, unsupervised clustering is performed in order to extract feature subspaces. Human activity sound models were trained using different combinations of these subspaces. The best performance 92.46% was achieved...
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