چکیده مقاله
K Means algorithm remains a basic algorithm within the field of data mining, and its linear time complexity has made it extremely popular However, its performance has remained sensitive to seed point selection, often ending up at local optima Although probabilistic seed selection using K Means is theoretically more effective, its results are still stochastic, thus requiring higher computations due to multiple passes over the dataset Moreover, traditional deterministic seed selection does not consider the divergent discriminatory capabilities of features while handling higher dimensional datasets, thus considering noise and signal features equivalently This paper proposes a new deterministic seed selection algorithm called IQR Weighted Initializer, where Interquartile Range values are used to weigh feature importance while choosing seed points By assigning higher importance values to structural features with large dispersal, the algorithm suppresses the effects of outliers Experimentation on 10 UCI datasets shows that the proposed algorithm performs better than current best deterministic and hierarchical algorithms Moreover, on datasets where outliers are pertinent, like Glass Identification, the algorithm shows a reduction of approximately 7% Sum of Squared Errors over Bisecting KMeans, thus preventing the algorithm from becoming stuck at local optima, where hierarchical algorithms fail Additionally, Silhouette Score and Adjusted Rand Index validation shows that the algorithm groups features into better structured classes, closer to actual labels
کلیدواژهها
نویسندگان
Mohammad Hamzeei
Department of Computer Engineering Birjand University of Technology Birjand, Iran
Mostafa Sabzekar
Department of Computer Engineering Birjand University of Technology Birjand, Iran
شیوه ارجاع
Hamzeei, Mohammad and Sabzekar, Mostafa,1404,Structure-Aware Initialization for K-Means Clustering: An IQR-Weighted Approach to Mitigate Outlier Impact,The 4th International Conference and the 9th National Conference on Computers, Information Technology and Artificial Intelligence Applications,Ahvaz
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