2 research outputs found
Adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity
UzimajuÄi u obzir nezadovoljavajuÄe djelovanje grupiranja srodnog Å”irenja algoritma grupiranja, kada se radi o nizovima podataka složenih struktura, u ovom se radu predlaže prilagodljivi nadzirani algoritam grupiranja srodnog Å”irenja utemeljen na strukturnoj sliÄnosti (SAAP-SS). Najprije se predlaže nova strukturna sliÄnost rjeÅ”avanjem nelinearnog problema zastupljenosti niskoga ranga. Zatim slijedi srodno Å”irenje na temelju podeÅ”avanja matrice sliÄnosti primjenom poznatih udvojenih ograniÄenja. Na kraju se u postupak algoritma uvodi ideja eksplozija kod vatrometa. Prilagodljivo pretražujuÄi preferencijalni prostor u dva smjera, uravnotežuju se globalne i lokalne pretraživaÄke sposobnosti algoritma u cilju pronalaženja optimalne strukture grupiranja. Rezultati eksperimenata i sa sintetiÄkim i s realnim nizovima podataka pokazuju poboljÅ”anja u radu predloženog algoritma u usporedbi s AP, FEO-SAP i K-means metodama.In view of the unsatisfying clustering effect of affinity propagation (AP) clustering algorithm when dealing with data sets of complex structures, an adaptive semi-supervised affinity propagation clustering algorithm based on structural similarity (SAAP-SS) is proposed in this paper. First, a novel structural similarity is proposed by solving a non-linear, low-rank representation problem. Then we perform affinity propagation on the basis of adjusting the similarity matrix by utilizing the known pairwise constraints. Finally, the idea of fireworks explosion is introduced into the process of the algorithm. By adaptively searching the preference space bi-directionally, the algorithmās global and local searching abilities are balanced in order to find the optimal clustering structure. The results of the experiments with both synthetic and real data sets show performance improvements of the proposed algorithm compared with AP, FEO-SAP and K-means methods
Gravity Theory-Based Affinity Propagation Clustering Algorithm and Its Applications
The original Affinity Propagation clustering algorithm (AP) only used the Euclidean distance of data sample as the only standard for similarity calculation. This method of calculation had great limitations for data with high dimension and sparsity when the original algorithm was running. Due to the single calculation method of similarity, the convergence and clustering accuracy of the algorithm were greatly affected. On the other hand, in the universe, we can consider the formation of galaxies is a clustering process. In addition, the interaction between different celestial bodies are achieved through universal gravitation. This paper introduced the Density Peak clustering algorithm (DP) and gravitational thought into the AP algorithm, and constructed the density property to calculate the similarity, put forward the Affinity Propagation clustering algorithm based on Gravity (GAP). The proposed algorithm was more accurate to calculate similarity of simple points through the local density of corresponding points, and then used the gravity formula to update the similarity matrix. The data clustering process could be seen as the sample points spontaneously attract each other based on āgravitationā. Experimental results showed that the convergence performance of GAP algorithm is obviously improved over the AP algorithm, and the clustering effect was better