Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient

<div>Artificial dataset <span class="nowrap"><svg height="11.8174pt" id="M270" style="vertical-align:-3.1815pt" version="1.1" viewbox="-0.0498162 -8.6359 14.9762 11.8174" width="14.9762pt" 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="M743 375C743 570 607 650 407 650H136L130 622C214 612 220 606 204 526L125 122C110 43 105 36 24 27L17 0H250C382 0 482 23 571 76C680 141 743 248 743 375ZM644 379C644 188 520 36 305 36C277 36 249 38 229 46C205 55 202 71 212 126L293 553C300 588 306 600 317 606C330 613 356 617 390 617C547 617 644 537 644 379Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,9.893,3.132)"><path d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z"></path></g></svg>.</span></div>

Mathematical Problems in Engineering

tab3

Table 3

Table 3: Weighted k-Prototypes Clustering Algorithm Based on the Hybrid Dissimilarity Coefficient 