In general, clustering is defined as partitioning similar and dissimilar objects into several groups. It has been widely used in applications like pattern recognition, image processing, and data analysis. When the dataset contains some missing data or value, it is termed incomplete data. In such ...
Based on the data collected in the preliminary questionnaire, we have verified that the DTW clustering obtained is independent of external factors. The identification of two distinct clusters representing trained and non-trained individuals, along with the confirmation that this clustering is not ...
Clustering, a classical machine learning technique, is widely used in time series prediction. Li et al. [29] introduced logistic weighted dynamic time warping (LWDTW) as a similarity measure and trained LSTM with each cluster’s data. The performance of LSTM-LWDTW was better compared to RNN-...
此聚类算法是2014年发表在Science的一篇文章,"Clustering by fast search and find of density peaks",此算法是基于密度的一种聚类算法。传统的DBSCAN算法需要提前设定大量参数,且结果对阈值较敏感;而此算法对阈值更加鲁棒。 1. DP算法本质上是通过两个指标来确定聚类中心。一个是局部密度,即它认为聚类中心的局部密度...
fold-prediction 3https://bmi.inf.ethz.ch/supplements/ protsubloc/ 4www.robots.ox.ac.uk/˜vgg/data/flowers/17/ 5www.robots.ox.ac.uk/˜vgg/data/flowers/102/ 6http://kdd.ics.uci.edu/databases/reuters21578/ • Multiple kernel clustering with local alignment maximization (LKAM) [9]....
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Nonnegative Matrix Factorization (NMF) has received much attention in data clustering due to its intuitive parts-based interpretation [30], [31]. Previous studies have shown that NMF is essentially equal to k-means with a relaxed condition [30]. Here we start with an introduction to NMF [32...
When the clusters are hyper-spherical, hierarchical clustering is less effective than k means. Hierarchical clustering has many arbitrary judgments, rarely offers the best answer, does not perform well with incomplete information, performs badly with heterogeneous data types, performs poorly on massive ...
The usual practice with such data sets is to either impute the values under an assumption of a missing-at-random mechanism or to ignore the incomplete records, and then to use the desired clustering method. We develop an efficient version of the $k$-means algorithm that allows for clustering...