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Data clustering is a collection of data objects similar to one another within the same cluster and dissimilar to the objects in other clusters. The shuffled frog-leaping algorithm is a nature-inspired algorithm that mimics the natural biological evolution process of frogs. This algorithm also consists of elements like local search and exchanging in...
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... are, entropy, purity, completeness score (CS), homogeneity score (HS), and FMeasure. í µí°¸(í µí± í µí± ) Classes glass 214 9 6 wine 178 13 3 iris 150 4 3 seeds 210 7 3 heart 270 13 2 appendicitis 106 7 2 Table 2. ...Context 2
... H(P) is the cluster Entropy and H(P|T) is the clusters conditional Entropy. Tables 3 and 9 presents the results obtained with the iris data set. From the tables, we have observed that the proposed SFLA, GWO and FFA produce best results for TW fitness function. ...Context 3
... are, entropy, purity, completeness score (CS), homogeneity score (HS), and FMeasure. í µí°¸(í µí± í µí± ) Classes glass 214 9 6 wine 178 13 3 iris 150 4 3 seeds 210 7 3 heart 270 13 2 appendicitis 106 7 2 Table 2. ...Citations
... Another research, Isnanto et al. [33] proved that the accuracy of the K-means method is better than other methods. The K-means method puts similar data into the same labels [34]. ...
... Labeling can use the K-means method. The K-means method can group similar data into data with the same label [34]. The calculation of the K-means method can be shown as [31], [38]. ...
span>The Indonesian government often needs assistance in making citizen-based decisions, for example selecting work program plans. Residents have their criteria in the forum to choose a work program plan. This study proposes the K-means fuzzy learning vector quantization (FLVQ) methods to select citizen-based government decision-making criteria. The K-means FLVQ method has never been used to assist government decision-making. However, citizen criteria can be a success factor for government decision-making. The selection of criteria begins with data collection from forum participants. The results of data collection get 11 criteria. Then, the K-means FLVQ method carries out labeling and classification. The addition of the K-means process in the selection criteria can provide optimal results. Citizens can give assessment criteria freely. Then the assessment of citizens is classified by FLVQ. The classification results obtained seven criteria, namely: i) urgency, ii) sustainability, iii) priority, vi) usability, v) prosperity, vi) comfortability, and vii) artistic. Governments can use these criteria to make decisions about planned work programs. The criteria selection algorithm was also evaluated using the confusion matrix method with an accuracy of 88% and an error of 12%.</span
