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Recommender systems help to overcome the problem of information overload on the Internet by providing personalized recommendations to the users. Content-based filtering and collaborative filtering are usually applied to predict these recommendations. Among these two, Collaborative filtering is the most common approach for designing e-commerce recommender systems. Two major challenges for CF based...
A significant problem in multi-sensor multi-target tracking system is measurement to track association. Based on fuzzy clustering means algorithm, an efficient algorithm has been proposed to solve this problem. The fuzzy clustering means data association (FCMDA) algorithm has better performance than the other already known fuzzy logic data association algorithms. However, it is still worthy to investigate...
In the last few years the gene expression microarray technology has become a central tool in the field of functional genomics in which the expression levels of thousands of genes in a biological sample are determined in a single experiment. Several clustering and biclustering methods have been introduced to analyze the gene expression data by identifying the similar patterns and grouping genes into...
We report on three distinct experiments that provide new valuable insights into learning algorithms and datasets. We first describe two effective meta-features that significantly impact the predictive accuracy of a broad range of learning algorithms. We then introduce a new efficient meta-feature that measures the degree of hardness (or difficulty) of datasets and show that it is highly linearly correlated...
In this paper, a new dynamic clustering algorithm based on random sampling is proposed. The algorithm addresses well known challenges in clustering such as dynamism, stability, and scaling. The core of the proposed method isbased on the definition of a function, named the Oracle,which can predict whether two random data points belongto the same cluster or not. Furthermore, this algorithm isalso equipped...
An unsupervised machine learning method-clustering, is introduced to conclude characteristics of vessel traffic flow data. A new way is found to implement data analysis in vessel traffic field using artificial intelligent technique. A similarity based algorithm, K-means, is selected in the clustering process for its simplicity and efficiency and a popular data mining tool named WEKA is chosen to execute...
In order to achieve the on-line traffic flow prediction, a novel multi-mode prediction method based on mode classification off-line is proposed. Firstly, the elevator traffic flow is classified off-line into patterns by the two-stage Clustering Algorithm, Artificial Immune C-Means Clustering Algorithm (AI C-Means CA). Then Gaussian Mixture Model (GMM) is used to model the multi-mode elevator traffic...
We present a novel framework that applies a meta-learning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candidate algorithms that could be used with that dataset. This ranking could, among other things, support non-expert users in the algorithm selection task. In order to evaluate the framework proposed, we implement a prototype that employs...
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