Most clustering algorithms were designed with certain assumptions and it is difficult to find natural clustering in a point cloud which contains lots of noise and unknown number of clusters with nature shapes. In this paper, we propose a clustering algorithm based both on density and spatial ...
Some characteristics and week points of traditional density-based clustering algorithms are deeply analysed , then an improved way based on density distribution function is put forward. K Nearest Neighbor( KNN ) is used to measure the density of each point, then a local maximum density point is ...
This implies that the method we suggest, unlike any pre-existing fair clustering algorithms, satisfies the correspondence principle, that is, the algorithm would still successfully detect clusters based on distribution even if the fairness constraint is removed. The contribution of our paper is ...
Advanced satellite tracking technologies provide biologists with long-term location sequence data to understand movement of wild birds then to find explicit correlation between dynamics of migratory birds and the spread of avian influenza. In this paper, we propose a hierarchical clustering algorithm base...
Another study explored effective prediction models for discharge planning based on a machine learning algorithm derived from data gathered during hospitalization in the acute phase of ischemic stroke. That study found that the use of clustering learning algorithms permits the unsupervised identification of ...
To address the shortcomings of the EM algorithm, Reference [188] proposes a method based on genetic algorithms to solve for the parameters of the GMM. To solve PLF [189,190], an alternative method is to centralize the calculations utilizing a gradient descent approach, such as the Newton-...
Margin-based clustering is one of the most classical clustering algorithms, which assumes that the best clustering structure can be determined by introducing margin used in supervised learning. That is for a satisfactory clustering result, when used as labels for supervised learning, some margin-relate...
The second term in the denominator of (53) becomes large for outliers, thus yielding small membership values and improving robustness of prototype-based clustering algorithms. To further improve robustness, we propose the application of metrics in the NC approach. Substituting the norm for in (51)...
The proposed method involves a novel strategy to vectorize genomes based on their gene distribution. A number of existing subspace clustering and biclustering algorithms were evaluated to identify the best framework upon which to develop our algorithm; we extended a generic subspace clustering algorithm ...
An algorithm based on Java implementation, can automatically check the set of outliers in a set of data, eliminate these outliers, and finally get normal data.基于java实现的能够自动检查出一组数据中的异常值的集合,剔除这些异常集,得到正常数据。