Abstract
Clustering is a technique in which the data are categorized based on their similarities and differences. In a cluster the distribution of objects around the center point and also their performance and function is different. In this paper, a new approach is presented to make improvement inter-cluster and intra-cluster of objects based on clustering that is not addressed in the literature review. Potential objects are detected and improved in micro and macro levels. The advantage of this approach is its ability to determine the thresholds dynamically according to the objectives and scope of the problem. The ability and usefulness of the proposed approach were examined on a data set of American household energy consumption. The results of applying this algorithmic approach indicate that it can improve the cluster objects with optimal changes and, in general, improve the performance of the entire data set. These results indicate the capability and efficiency of the approach in improvement the function and performance of clusters’ objects.
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References
Aliahmadipour L, Torra V, Eslami E (2017) On hesitant fuzzy clustering and clustering of hesitant fuzzy data. In: Fuzzy sets, rough sets, multisets and clustering, pp 157–168
Araújo E, Chaves A, Lorena L (2019) Improving the clustering search heuristic: an application to cartographic labeling. Appl Soft Comput 77:261–273
Ashouri M, Haghighat F, Fung B, Yoshino H (2019) Development of a ranking procedure for energy performance evaluation of buildings based on occupant behavior. Energy Buildings 183:659–671. https://doi.org/10.1016/j.enbuild.2018.11.050
Ashouri M, Fung BCM, Haghighat F, Yoshino H (2020) Systematic approach to provide building occupants with feedback to reduce energy consumption. Energy 194:116813
Bojic I, Lipic T, Podobnik V (2012) Bio-inspired clustering and data diffusion in machine social networks. In Computational social networks. Springer, London, pp 51–79
Campus.datacamp.com (2020) Between group sum of squares | R. https://campus.datacamp.com/courses/intro-to-statistics-with-r-analysis-of-variance-anova/chapter-one-an-introduction-to-anova?ex=8. Accessed 4 June 2020
Capozzoli A, Serale G, Piscitelli MS, Grassi D (2017) Data mining for energy analysis of a large data set of flats. Proc Inst Civ Eng Eng Sustain 170(1):3–18
Cunha DS, Cruz DP, Politi A, Castro LN, Maia RD (2017) Bio-inspired multi objective clustering optimization: a survey and a proposal. Artif Intell Res 6:10–26
Danish MSS, Senjyu T, Ibrahimi AM, Ahmadi M, Howlader AM (2019) A managed framework for energy-efficient building. J Build Eng 21:120–128
Eia.gov (2018) About EIA—U.S. Energy Information Administration (EIA). U.S. Energy Information Administration (EIA). https://www.eia.gov/about/. Accessed 15 Jan 2018
Ester M, Kriegel HP, Sander J (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining, pp 226–231.
Everitt BS, Landau S, Leese M, Stahl D (2011) Cluster analysis 5. Wiley series in probability and statistics
Fathian M, Amiri B (2007) A honey-bee mating approach on clustering. Int J Adv Manuf Technol 38(7–8):809–821
Hernández L, Baladrón C, Aguiar J, Carro B, Sánchez-Esguevillas A (2012) Classification and clustering of electricity demand patterns in industrial parks. Energies 5(12):5215–5228
IEEE Industry Applications Society (1991) Power Systems Engineering Committee. IEEE Recommended Practice for Electric Power Systems in Commercial Buildings; American National Standards Institute, New York
Jabeur N, Yasar A, Shakshuki E, Haddad H (2020) Toward a bio-inspired adaptive spatial clustering approach for IoT applications. Future Gener Comput Syst 107:736–744
Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recognit Lett 31:651–666
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323
Kao YT, Zahara E, Kao IW (2008) A hybridized approach to data clustering. Expert Syst Appl 34(3):1754–1762
Khanmohammadi S, Adibeig N, Shanehbandy S (2017) An improved overlapping k-means clustering method for medical applications. Expert Syst Appl 67:12–18
Li K, Ma Z, Robinson D, Ma J (2018) Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering. Appl Energy 231:331–342. https://doi.org/10.1016/j.apenergy.2018.09.050
Lu SY, Fu KS (1978) A sentence-to-sentence clustering procedure for pattern analysis. IEEE Trans Syst Man Cybern 8:381–389
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Fifth Berkeley symposium on mathematics, statistics and probability, University of California Press, pp 281–297
Murtagh F (1983) A survey of recent advances in hierarchical clustering algorithms. Comput J 26(4):354–359
Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evolut Comput 16:1–18
Nazeriye M, Haeri A, Martínez-Álvarez F (2020) Analysis of the impact of residential property and equipment on building energy efficiency and consumption—a data mining approach. Appl Sci 10:3589
Niknam T, Olamaie J, Amiri B (2008) A hybrid evolutionary algorithm based on ACO and SA for cluster analysis. J Appl Sci 8(15):2695–2702
Niknam T, Amiri B, Olamaie J, Arefi A (2009) An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering. J Zhejiang Univ Sci 10(4):512–519
Nrcan.gc.ca (2020) Energy efficiency trends in Canada 1990 to 2013, 2013. Natural Resources Canada, pp 11–18. https://www.nrcan.gc.ca/energy/publications/19030. Accessed 3 June 2020
Ritter H, Martinetz T, Schulten K, Barsky D, Tesch M, Kates R (1992) Neural computation and self-organizing maps: an introduction. Addison-Wesley, Reading, pp 141–161. http://www.ks.uiuc.edu/Overview/KS/book.html. Accessed 3 June 2020
Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65
Statistics How To (2015) Within mean square: definition and formula—statistics how to. https://www.statisticshowto.com/within-mean-square/. Accessed 4 June 2020
Sun H, Wang S, Jiang Q (2004) FCM-based model selection algorithms for determining the number of clusters. Pattern Recognit 37(10):2027–2037
The Spss.ch (2020) The SPSS TwoStep cluster component, Technical report. https://www.spss.ch/upload/1122644952_The%20SPSS%20TwoStep%20Cluster%20Component.pdf. Accessed 3 June 2020
Wang J, Cao J, Li B, Lee S, Sherratt RS (2015) Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks. IEEE Trans Consum Electron 61(4):438–444
Yang M, Nataliani Y (2017) Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters. Pattern Recognit 71:45–59
Yang M, Chang-Chien S, Nataliani Y (2019) Unsupervised fuzzy model-based Gaussian clustering. Inf Sci 481:1–23
Yasojima C, Ramos T, Araujo T, Meiguins B, Neto N, Morais J (2019) Evaluation of bio-inspired algorithms in cluster-based kriging optimization. In: Computational science and its applications—ICCSA 2019, pp 731–744
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The authors thank the reviewers for their valuable suggestions for improving the manuscript. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Nazeriye, M., Haeri, A. Proposing a new clustering approach aimed to energy consumption improvement. J Ambient Intell Human Comput 14, 15831–15849 (2023). https://doi.org/10.1007/s12652-020-02743-z
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DOI: https://doi.org/10.1007/s12652-020-02743-z