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Design and Performance Evaluation of Efficient Clustering Algorithms for Big Data Applications

Ping Dai

Ping Dai

Anhui Institute of Information TechnologyWuhu, China
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Dai, Ping
, 
Jinhua Wu

Jinhua Wu

Anhui Institute of Information TechnologyWuhu, China
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Wu, Jinhua
 and 
Hao He

Hao He

Anhui Institute of Information TechnologyWuhu, China
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He, Hao
  
Feb 05, 2025
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Published Online: Feb 05, 2025
Received: Sep 22, 2024
Accepted: Dec 31, 2024
DOI: https://doi.org/10.2478/amns-2025-0056
Keywords
© 2025 Ping Dai et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Data distribution and sample data distribution
Data distribution and sample data distribution

Figure 2.

Expansionary assessment
Expansionary assessment

Figure 3.

Parametric sensitivity analysis
Parametric sensitivity analysis

Figure 4.

A region of the first six of the population of a city
A region of the first six of the population of a city

Figure 5.

The change of people’s mouth in a region
The change of people’s mouth in a region

Figure 6.

The number of people in a region of 2023
The number of people in a region of 2023

Figure 7.

The cluster center is shown on the map
The cluster center is shown on the map

Execution time comparison on seven data sets

Data sets Size(MB) Execution time(s)
K-means ParCLARA Par2PK-Means Ours
Taxi trajectory 80 78 658 432 310
150 100 755 480 375
310 190 892 578 400
650 1120 1175 830 615
1280 2365 1665 1350 1025
2560 - 2735 2412 1875
Iris 80 30 610 300 210
150 55 690 375 275
310 120 815 450 300
650 680 1035 640 405
1280 1750 1200 1001 765
2560 - 2455 2015 1420
Haberman’s survival 80 50 576 312 295
150 65 677 400 310
310 125 834 480 375
650 715 981 675 425
1280 2020 1325 1250 975
2560 - 2445 2200 1725
Ecoli 80 35 620 350 281
150 60 715 400 326
310 125 842 455 370
650 755 1123 670 485
1280 1925 1635 1230 765
2560 - 2578 2210 1420
Hayes-roth 80 40 567 312 275
150 55 640 390 340
310 120 725 470 385
650 725 962 650 435
1280 1985 1475 1200 735
2560 - 2430 2100 1577
Lenses 80 30 620 300 295
150 62 705 385 325
310 135 925 450 375
650 720 1052 540 430
1280 1890 1345 1080 815
2560 - 2375 2085 1475
Wine 80 55 625 360 310
150 76 700 410 355
310 125 834 460 400
650 732 1000 600 425
1280 2200 1345 1240 845
2560 - 2564 2250 1625

Data structure

Name Type Remark
Id_Num String Id Number
Create_Time Date Create (Upload) Time
Lng Double Longitude
Lat Double Latitude
Language:
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Journal Subjects:
Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other
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