[Retracted] Detection of Power Data Outliers Using Density Peaks Clustering Algorithm Based on <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.06580067pt" id="M1" height="11.4652pt" version="1.1" viewBox="-0.0657574 -11.3994 13.1359 11.4652" width="13.1359pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-76" d="M743 650H503L496 622L527 618C563 613 564 603 532 573C449 495 371 431 323 392C301 374 272 355 246 346L280 522C297 609 300 614 379 622L385 650H135L129 622C209 614 215 609 198 522L124 133C106 39 99 35 23 28L17 0H271L277 28C193 35 192 39 208 133L239 316C264 328 280 325 303 288C368 183 435 90 502 0H652L659 28C602 34 584 43 543 94C495 154 403 283 347 369L574 554C634 603 659 612 735 624L743 650Z"/></g></svg>-</span>Nearest Neighbors

<div>Daily load profile of 10 transformers during 6 days.</div>

Wireless Communications and Mobile Computing

fig4

Figure 4

Figure 4: [Retracted] Detection of Power Data Outliers Using Density Peaks Clustering Algorithm Based on <span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-0.06580067pt" id="M1" height="11.4652pt" version="1.1" viewBox="-0.0657574 -11.3994 13.1359 11.4652" width="13.1359pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g113-76" d="M743 650H503L496 622L527 618C563 613 564 603 532 573C449 495 371 431 323 392C301 374 272 355 246 346L280 522C297 609 300 614 379 622L385 650H135L129 622C209 614 215 609 198 522L124 133C106 39 99 35 23 28L17 0H271L277 28C193 35 192 39 208 133L239 316C264 328 280 325 303 288C368 183 435 90 502 0H652L659 28C602 34 584 43 543 94C495 154 403 283 347 369L574 554C634 603 659 612 735 624L743 650Z"/></g></svg>-</span>Nearest Neighbors 

Wireless Communications and Mobile Computing

[Retracted] Detection of Power Data Outliers Using Density Peaks Clustering Algorithm Based on -Nearest Neighbors

Figure 4