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where > 0 de nes the scale of dissimilarity between items ya and yb The constant determines when two items should, or should not be located next to each other If is too large, it results in the fusion of individual clusters, clustering items together that do not belong together If is too small, many small clusters are formed Items that belong together are not clustered together It is thus clear that has a direct in uence on the number of clusters formed The dissimilarity constant also has an in uence on the speed of the clustering process The larger , the faster the process is Using the measure of similarity, (ya ), the picking up and dropping probabilities are de ned as [540] Pp (ya ) = Pd (ya ) = 1 1 + (ya ) 2 (ya ) if (ya ) < 2 1 if (ya ) 2

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(1746) (1747)

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A summary of the Lumer Faieta ant colony clustering algorithm is given in Algorithm 177 The following aspects of Algorithm 177 need clari cation: nt is the maximum number of iterations The grid size: There should be more sites than data vectors, since items are not stacked Each site may contain only one item If the number of sites is approximately the same as the number of items, there are not enough free cells available for ants to move to Large clusters are formed with a high classi cation error On the other hand, if there are many more sites than items, many small clusters may be formed Also, clustering takes long due to the large distances that items need to be transported The number of ants: There should be fewer ants than data vectors If there are too many ants, it may happen that most of the items are carried by ants Consequently, density calculation results in close to zero values of (ya ) Dropping probabilities are then small, and most ants keep carrying their load without depositing it Clusters may never be formed On the other hand, if there are too few ants, it takes longer to form clusters Local density: Consider that an ant carries item ya If the n2 neighboring N sites have items similar to ya , (ya ) 1 Item ya is then dropped with high probability (assuming that the site is empty) On the other hand, if the items are very dissimilar from ya , (ya ) 0, and the item has a very low probability of being dropped In the case of an unladen ant, the pick-up probability is large

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Algorithm 177 Lumer Faieta Ant Colony Clustering Algorithm Place each data vector ya randomly on a grid; Place nk ants on randomly selected sites; Initialize values of 1 , 2 , and nt ; for t = 1 to nt do for each ant, k = 1, , nk do if ant k is unladen and the site is occupied by item ya then Compute (ya ) using equation (1745); Compute Pp (ya ) using equation (1746); if U (0, 1) Pp (ya ) then Pick up item ya ; end end else if ant k carries item ya and site is empty then Compute (ya ) using equation (1745); Compute Pd (ya ) using equation (1747); if U (0, 1) Pd (ya ) then Drop item ya ; end end end Move to a randomly selected neighboring site not occupied by another ant; end end

division, which pays its employees once a week She earned $462 in the past week, and has claimed three withholding allowances Using the wage bracket table in Exhibit 82, we can find the correct wage bracket that contains her pay range (of at least $460 but less than $470), and then shift horizontally across the table from that wage bracket to the column for three withholding allowances, which shows that her total taxes should be $6557

Patch size, nN : A larger patch size allows an ant to use more information to make its decision Better estimates of local densities are obtained, which generally improve the quality of clusters However, a larger patch size is computationally more expensive and inhibits quick formation of clusters during the early iterations Smaller sizes reduce computational e ort, and less information is used This may lead to many small clusters

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