A New Binary Adaptive Elitist Differential Evolution Based Automatic<i> k</i>-Medoids Clustering for Probability Density Functions

<table class="fixed-width table-group" id="tab8"><tr><td><table class="table"><colgroup><col style="width:7.62em"/><col style="width:4.42em"/><col style="width:2.24em"/><col style="width:5.47em"/><col style="width:4.42em"/><col style="width:2.24em"/><col style="width:3.00em"/></colgroup><tr><td class="thead-hr" colspan="7"><hr/></td></tr><tr class="thead"><td class="align_left" rowspan="2"> </td><td class="align_center" colspan="3">Kolmogorov-<svg height="10.1334pt" id="M84" style="vertical-align:-0.2455101pt" version="1.1" viewbox="-0.0498162 -9.88789 49.678 10.1334" width="49.678pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M409 504C401 567 396 607 392 642C354 654 312 665 266 665C137 665 60 583 60 487C60 374 161 325 225 290C300 250 355 215 355 141C355 68 311 21 235 21C131 21 86 122 71 183L41 176C48 128 61 42 68 21C78 16 93 8 118 0C142 -7 175 -15 216 -15C349 -15 438 69 438 174C438 287 344 333 265 374C186 414 138 449 138 522C138 576 172 631 249 631C336 631 363 562 380 499L409 504Z"></path></g><g transform="matrix(.013,0,0,-0.013,6.084,0)"><path d="M797 0V26C739 32 732 36 732 103V296C732 394 682 449 605 449C576 449 550 437 529 423C504 407 475 389 446 366C425 418 382 449 334 449C303 449 279 437 253 421C222 403 201 385 180 371V452C135 432 85 419 41 411V388C99 379 102 374 102 310V103C102 38 93 32 27 26V0H238V26C189 32 180 38 180 103V338C210 363 250 390 289 390C351 390 377 348 377 275V103C377 37 368 32 306 26V0H520V26C465 32 456 38 456 101V296C456 314 455 326 453 338C491 369 529 390 565 390C628 390 653 345 653 274V107C653 36 642 32 583 26V0H797Z"></path></g><g transform="matrix(.013,0,0,-0.013,16.731,0)"><path d="M135 536C164 536 186 560 186 587C186 617 164 639 136 639C109 639 85 617 85 587C85 560 109 536 135 536ZM252 0V26C188 32 181 38 181 106V451C138 433 90 420 39 412V388C99 379 102 374 102 312V106C102 38 95 32 32 26V0H252Z"></path></g><g transform="matrix(.013,0,0,-0.013,20.215,0)"><path d="M181 342V451C133 431 89 419 40 411V388C98 381 102 377 102 311V104C102 38 95 32 33 26V0H263V26C186 32 181 38 181 104V287C203 343 235 372 261 372C277 372 289 366 304 352C310 346 318 345 330 350C349 359 362 379 362 399C362 422 338 449 304 449C256 449 213 393 183 342H181Z"></path></g><g transform="matrix(.013,0,0,-0.013,25.116,0)"><path d="M524 0V26C466 32 460 36 460 104V297C460 393 411 449 331 449C302 449 276 437 248 419C223 402 201 387 181 372V451C137 432 90 420 42 411V388C96 378 102 374 102 310V104C102 38 97 33 29 26V0H246V26C187 32 181 36 181 104V339C211 365 250 390 290 390C357 390 381 345 381 276V109C381 40 374 32 315 26V0H524Z"></path></g><g transform="matrix(.013,0,0,-0.013,32.175,0)"><path d="M257 449C165 449 37 374 37 209C37 98 119 -12 256 -12C355 -12 473 65 473 226C473 349 381 449 257 449ZM244 416C333 416 380 320 380 204C380 67 329 21 267 21C184 21 130 115 130 241C130 354 184 416 244 416Z"></path></g><g transform="matrix(.013,0,0,-0.013,38.596,0)"><path d="M478 437H300V411C357 404 363 393 347 345C325 280 293 190 258 107C229 178 188 284 163 354C147 397 149 405 203 411V437H-2V411C52 403 59 396 82 339C130 222 176 109 220 -11H248C304 138 356 261 390 336C417 395 425 404 478 411V437Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,44.979,-5.741)"><path d="M442 39L431 68C420 63 406 57 394 57C377 57 359 71 359 118V301C359 353 349 393 314 424C291 443 261 451 230 451C171 438 107 401 79 381C59 366 46 355 46 340C46 316 72 295 91 295C106 295 117 303 122 319C131 350 139 371 152 384C163 397 178 404 197 404C246 404 281 365 281 293V278C260 262 186 232 124 213C69 194 41 161 41 112C41 47 92 -12 163 -12C195 -12 243 25 283 51C288 35 293 22 308 7C319 -4 339 -12 354 -12L442 39ZM281 87C262 68 226 51 199 51C169 51 129 74 129 126C129 162 151 181 190 198C211 208 259 229 281 240V87Z"></path></g></svg></td><td class="align_center" colspan="3">Shapiro-Wilk</td></tr><tr class="thead"><td class="align_center">Statistic</td><td class="align_center">df</td><td class="align_center">Sig.</td><td class="align_center">Statistic</td><td class="align_center">df</td><td class="align_center">Sig.</td></tr><tr><td class="thead-hr" colspan="7"><hr/></td></tr><tr><td class="align_left">CUReT data set</td><td class="align_center">.523</td><td class="align_center">50</td><td class="align_center"><i>.000</i></td><td class="align_center">.380</td><td class="align_center">50</td><td class="align_center"><i>.000</i></td></tr><tr class="table-tr"><td colspan="7"><hr class="tbody-hr"/></td></tr></table></td></tr><tr class="table-fn"><td><div>a. Lilliefors Significance Correction<br/></div></td></tr></table>

<div>Test of normality with significant level 0.05 for ARI value derived from baeDE-CDF for CUReT data set.</div>

Mathematical Problems in Engineering

tab8

Table 8

Table 8: A New Binary Adaptive Elitist Differential Evolution Based Automatic<i> k</i>-Medoids Clustering for Probability Density Functions 

Mathematical Problems in Engineering

A New Binary Adaptive Elitist Differential Evolution Based Automatic k-Medoids Clustering for Probability Density Functions

Table 8