Finding Best Clustering For Big Networks with Minimum Objective Function by Using Probabilistic Tabu Search

Yaqoob, Ali Falah; Al-Sarray, Basad

doi:10.24996/ijs.2019.60.8.21

Cited by 2 publications

(1 citation statement)

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“…The study presented an explanation of how to improve the security and safety of transmitting information and messages over the Internet by hiding data within clusters by adopting the Least Significant Bit (LSB) method 9 . A hybrid algorithm was proposed to improve the work of the FCM fuzzy clustering algorithm by adopting the Tabu probabilistic heuristic algorithm and finding a global clustering based on the value of the objective function at its minimum, and the results showed the superiority of the Tabu-FCM hybrid algorithm 10 . The study addressed the problem of image retrieval by improving the accuracy and speed of the retrieval system by applying the Fuzzy C Means (FCM) clustering algorithm to reduce the search space and speed up the image retrieval process.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Fuzzy C-Means Clustering with a Novel Standard Deviation Weighted Distance Measure

Mohammed,

Ashour

2024

Baghdad Sci.J

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The aim of this paper is to present a new approach to address the Fuzzy C Mean algorithm, which is considered one of the most important and famous algorithms that addressed the phenomenon of uncertainty in forming clusters according to the overlap ratios. One of the most important problems facing this algorithm is its reliance primarily on the Euclidean distance measure, and by nature, the situation is that this measure makes the formed clusters take a spherical shape, which is unable to contain complex or overlapping cases. Therefore, this paper attempts to propose a new measure of distance, where we were able to derive a formula for the variance of the fuzzy cluster to be entered as a weight on the Euclidean Distance (WED) formula. Moreover, the calculation was processed partitions matrix through the use of the K-Means algorithm and creating a hybrid environment between the fuzzy algorithm and the sharp algorithm. To verify what was presented, experimental simulation was used and then applied to reality using environmental data for the physical and chemical examination of water testing stations in Basra Governorate. It was proven through the experimental results that the proposed distance measure Weighted Euclidean distance had the advantage over improving the work of the HFCM algorithm through the criterion (Obj_Fun, Iteration, Min_optimization, good fit clustering and overlap) when (c = 2,3) and according to the simulation results, c = 2 was chosen to form groups for the real data, which contributed to determine the best objective function (23.93, 22.44, 18.83) at degrees of fuzzing (1.2, 2, 2.8), while according to the degree of fuzzing (m = 3.6), the objective function for Euclidean Distance (ED) was the lowest, but the criteria were (Iter. = 2, Min_optimization = 0 and ) which confirms that (WED) is the best.

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Section: Introductionmentioning

confidence: 99%