J-MEANS: A new local search heuristic for minimum sum of squares clustering. A new local search heuristic, called J-Means, is proposed for solving the minimum sum of squares clustering problem. The neighborhood of the current solution is defined by all possible centroid-to-entity relocations followed by corresponding changes of assignments. Moves are made in such neighborhoods until a local optimum is reached. The new heuristic is compared with two other well-known local search heuristics, K- and H-Means as well as with H-Means+, an improved version of the latter in which degeneracy is removed. Moreover, another heuristic, which fits into the variable neighborhood search metaheuristic framework and uses J-Means in its local search step, is proposed too. Results on standard test problems from the literature are reported. It appears that J-Means outperforms the other local search methods, quite substantially when many entities and clusters are considered.

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  1. Brusco, Michael J.; Steinley, Douglas: A variable neighborhood search heuristic for nonnegative matrix factorization with application to microarray data (2022)
  2. Çakırgil, Seray; Yücel, Eda; Kuyzu, Gültekin: An integrated solution approach for multi-objective, multi-skill workforce scheduling and routing problems (2020)
  3. Kazakovtsev, Lev; Rozhnov, Ivan; Shkaberina, Guzel; Orlov, Viktor: (k)-means genetic algorithms with greedy genetic operators (2020)
  4. Rezaei, Mohammad: Improving a centroid-based clustering by using suitable centroids from another clustering (2020)
  5. Shkaberina, Guzel Sh.; Orlov, Viktor I.; Tovbis, Elena M.; Kazakovtsev, Lev A.: On the optimization models for automatic grouping of industrial products by homogeneous production batches (2020)
  6. Zhou, Qing; Benlic, Una; Wu, Qinghua: A memetic algorithm based on reformulation local search for minimum sum-of-squares clustering in networks (2020)
  7. Baykasoğlu, Adil; Gölcük, İlker; Özsoydan, Fehmi Burçin: Improving fuzzy c-means clustering via quantum-enhanced weighted superposition attraction algorithm (2019)
  8. Vié, Marie-Sklaerder; Zufferey, Nicolas; Cordeau, Jean-François: Solving the wire-harness design problem at a European car manufacturer (2019)
  9. Aloise, Daniel; Castelo Damasceno, Nielsen; Mladenović, Nenad; Nobre Pinheiro, Daniel: On strategies to fix degenerate (k)-means solutions (2017)
  10. Chen, Binhui; Qu, Rong; Bai, Ruibin; Ishibuchi, Hisao: An investigation on compound neighborhoods for VRPTW (2017)
  11. Đorić, Danijela; Ait El Cadi, Abdessamad; Hanafi, Saïd; Mladenović, Nenad; Artiba, Abdelhakim: Clustering approach in maintenance of capillary railway network (2017)
  12. Nikolaev, Alexey; Mladenović, Nenad; Todosijević, Raca: J-means and I-means for minimum sum-of-squares clustering on networks (2017)
  13. Rusetskaya, Olga: Grouping cities based of their socio-economic indicators (2017)
  14. Todosijević, Raca; Urošević, Dragan; Mladenović, Nenad; Hanafi, Saïd: A general variable neighborhood search for solving the uncapacitated (r)-allocation (p)-hub Median problem (2017)
  15. Aloise, Daniel; Araújo, Arthur: A derivative-free algorithm for refining numerical microaggregation solutions (2015)
  16. Carrizosa, Emilio; Alguwaizani, Abdulrahman; Hansen, Pierre; Mladenović, Nenad: New heuristic for harmonic means clustering (2015)
  17. Zhikharevich, B. S.; Rusetskay, O. V.; Mladenović, N.: Clustering cities based on their development dynamics and variable neigborhood search (2015)
  18. Carrizosa, Emilio; Mladenović, Nenad; Todosijević, Raca: Variable neighborhood search for minimum sum-of-squares clustering on networks (2013)
  19. Hung, Cheng-Huang; Chiou, Hua-Min; Yang, Wei-Ning: Candidate groups search for K-harmonic means data clustering (2013)
  20. Alguwaizani, Abdulrahman: Degeneracy on (K)-means clustering (2012)

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