An Adaptive Density Peak Clustering Algorithm

doi:10.12068/j.issn.1005-3026.2022.06.001

Abstract

Abstract: The density peak clustering algorithm cannot adaptively cluster because it cannot adaptively select the clustering center and cutoff distance d_c according to data set， so that an adaptive density peak clustering(ADPC) algorithm was proposed. Firstly， a parameter μ_i that comprehensively considers the local density ρ_i and cutoff distance δ_i was proposed， and the cluster center was automatically determined according to the sorting and downtrend of μ_i. Then， an adaptive selection of d_c was made based on the concept of Gini coefficient. Finally， the ADPC algorithm was verified and compared with the DPC and K-means algorithm. The experimental results show that the ADPC algorithm has higher ARI， NMI and AC values， and has a better clustering effect.

Key words: clustering; adaptive; clustering center; cutoff distance; downward trend; Gini coefficient

CLC Number:

TP391.4

MA Shu-hua， YOU Hai-rong， TANG Liang， HE Ping. An Adaptive Density Peak Clustering Algorithm[J]. Journal of Northeastern University(Natural Science), 2022, 43(6): 761-768.

References

[1]Boomija M D，Phil M.Comparison of partition based clustering algorithms［J］.Journal of Computer Applications，2008，1(4):18-21.
[2]Krishna K，Narasimha Murty M.Genetic K-means algorithm［J］.IEEE Transactions on Systems，Man，and Cybernetics，Part B(Cybernetics)，1999，29(3):433-439.
[3]Park H S，Jun C H.A simple and fast algorithm for K-medoids clustering［J］.Expert Systems with Applications，2009，36(2):3336-3341.
[4]Huang Z，Ng M K.A fuzzy K-modes algorithm for clustering categorical data［J］.IEEE Transactions on Fuzzy Systems，1999，7(4):446-452.
[5]彭春春，陈燕俐，荀艳梅.支持本地化差分隐私保护的K-modes聚类方法［J］.计算机科学，2021，48(2):105-113.(Peng Chun-chun，Chen Yan-li，Xun Yan-mei.K-modes clustering guaranteeing local differential privacy［J］.Computer Science，2021，48(2):105-113.)
[6]Cardot H，Cénac P，Monnez J M.A fast and recursive algorithm for clustering large datasets with K-medians［J］.Computational Statistics & Data Analysis，2012，56(6):1434-1449.
[7]Tzortzis G F，Likas A C.The global kernel K-means algorithm for clustering in feature space［J］.IEEE Transactions on Neural Networks，2009，20(7):1181-1194.
[8]Wang J，Li M，Chen J，et al.A fast hierarchical clustering algorithm for functional modules discovery in protein interaction networks［J］.IEEE/ACM Transactions on Computational Biology and Bioinformatics，2010，8(3):607-620.
[9]Zhang T，Ramakrishnan R，Livny M.BIRCH:an efficient data clustering method for very large databases［J］.ACM Sigmod Record，1996，25(2):103-114.
[10]Kamal S，Burk L I.FACT:a new neural network-based clustering algorithm for group technology［J］.International Journal of Production Research，1996，34(4):919-946.
[11]Rani P.A survey on STING and CLIQUE grid based clustering methods［J］.International Journal of Advanced Research in Computer Science，2017，8(5):1510-1512.
[12]Meila M，Heckerman D.An experimental comparison of model-based clustering methods［J］.Machine Learning，2001，42(1):9-29.
[13]Zhang J，Yin Z，Wang R.Pattern classification of instantaneous cognitive task-load through gmm clustering，Laplacian eigen map，and ensemble svms［J］.IEEE/ACM Transactions on Computational Biology and Bioinformatics，2016，14(4):947-965.
[14]Kumar P，Varma K I，Sureka A.Fuzzy based clustering algorithm for privacy preserving data mining［J］.International Journal of Business Information Systems，2011，7(1):27-40.
[15]Bezdek J C，Ehrlich R，Full W.FCM:the fuzzy C-means clustering algorithm［J］.Computers & Geosciences，1984，10(2/3):191-203.
[16]Parimala M，Lopez D，Senthilkumar N C.A survey on density based clustering algorithms for mining large spatial databases［J］.International Journal of Advanced Science and Technology，2011，31(1):59-66.
[17]Schubert E，Sander J，Ester M，et al.DBSCAN revisited，revisited:why and how you should(still)use DBSCAN［J］.ACM Transactions on Database Systems，2017，42(3):1-21.
[18]Rodriguez A，Laio A.Clustering by fast search and find of density peaks［J］.Science，2014，344(6191):1492-1496.
[19]Milanovic B.A simple way to calculate the Gini coefficient，and some implications［J］.Economics Letters，1997，56(1):45-49.