Research Article
A Hybrid Method to Solve Data Sparsity in Travel Recommendation Agents Using Fuzzy Logic Approach
Table 5
1-way ANOVA for clusters versus input attributes.
| Attribute Y | Attribute X | Description | Statistical test |
| Rooms | EM cluster | Value | Examples | Average | Std-de | Variance decomposition | Segment-1 | 2200 | 3.8573 | 1.0203 | Source | Sum of square | d.f. | Segment-2 | 2960 | 3.9125 | 1.0210 | BSS | 17876.3754 | 7 | Segment-3 | 3213 | 2.5584 | 1.3308 | WSS | 27769.2741 | 22498 | Segment-4 | 2827 | 4.2565 | 0.7451 | TSS | 45645.6495 | 22505 | Segment-5 | 2963 | 1.6369 | 0.7105 | Significance level | Segment-6 | 1848 | 1.7413 | 0.7809 | Statistics | Value | Proba | Segment-7 | 3205 | 2.7735 | 1.3757 | Fisher’s F | 2069.001535 | ≤0.001 | Segment-8 | 3290 | 3.1827 | 1.4007 | | | | All | 22506 | 3.0102 | 1.4242 | | | |
| Value | EM cluster | Value | Examples | Average | Std-dev | Variance decomposition | Segment-1 | 2200 | 4.0577 | 0.8930 | Source | Sum of square | d.f. | Segment-2 | 2960 | 1.9155 | 0.8963 | BSS | 24632.8757 | 7 | Segment-3 | 3213 | 3.7893 | 1.1622 | WSS | 21107.1895 | 22498 | Segment-4 | 2827 | 1.8599 | 0.8723 | TSS | 45740.0653 | 22505 | Segment-5 | 2963 | 1.9433 | 0.9192 | Significance level | Segment-6 | 1848 | 1.9464 | 0.9347 | Statistics | Value | Proba | Segment-7 | 3205 | 3.5189 | 1.2520 | Fisher’s F | 3750.857613 | ≤0.001 | Segment-8 | 3290 | 4.4371 | 0.6405 | | | | All | 22506 | 2.9886 | 1.4256 | | | |
| Location | EM cluster | Value | Examples | Average | Std-dev | Variance decomposition | Segment-1 | 2200 | 4.2050 | 0.7690 | Source | Sum of square | d.f. | Segment-2 | 2960 | 2.1064 | 1.0750 | BSS | 21800.7256 | 7 | Segment-3 | 3213 | 1.7077 | 0.7495 | WSS | 23888.6228 | 22498 | Segment-4 | 2827 | 3.4574 | 1.2876 | TSS | 45689.3484 | 22505 | Segment-5 | 2963 | 3.8157 | 1.1281 | Significance level | Segment-6 | 1848 | 1.8874 | 0.9151 | Statistics | Value | Proba | Segment-7 | 3205 | 4.3339 | 0.7020 | Fisher’s F | 2933.092161 | ≤0.001 | Segment-8 | 3290 | 2.6751 | 1.3261 | | | | All | 22506 | 3.0317 | 1.4248 | | | |
| Service | EM cluster | Value | Examples | Average | Std-dev | Variance decomposition | Segment-1 | 2200 | 4.1536 | 0.7375 | Source | Sum of square | d.f. | Segment-2 | 2960 | 2.7689 | 1.3658 | BSS | 16952.6087 | 7 | Segment-3 | 3213 | 3.0479 | 1.3784 | WSS | 28567.3897 | 22498 | Segment-4 | 2827 | 3.1772 | 1.3609 | TSS | 45519.9984 | 22505 | Segment-5 | 2963 | 4.0192 | 0.9300 | Significance level | Segment-6 | 1848 | 1.8718 | 0.9482 | Statistics | Value | Proba | Segment-7 | 3205 | 1.4555 | 0.5539 | Fisher’s F | 1907.268560 | ≤0.001 | Segment-8 | 3290 | 3.4596 | 1.2705 | | | | All | 22506 | 3.0003 | 1.4222 | | | |
| Cleanliness | EM cluster | Value | Examples | Average | Std-dev | Variance decomposition | Segment-1 | 2200 | 4.1536 | 0.7375 | Source | Sum of square | d.f. | Segment-2 | 2960 | 2.7689 | 1.3658 | BSS | 16952.6087 | 7 | Segment-3 | 3213 | 3.0479 | 1.3784 | WSS | 28567.3897 | 22498 | Segment-4 | 2827 | 3.1772 | 1.3609 | TSS | 45519.9984 | 22505 | Segment-5 | 2963 | 4.0192 | 0.9300 | Significance level | Segment-6 | 1848 | 1.8718 | 0.9482 | Statistics | Value | Proba | Segment-7 | 3205 | 1.4555 | 0.5539 | Fisher’s F | 1907.268560 | ≤0.001 | Segment-8 | 3290 | 3.4596 | 1.2705 | | | | All | 22506 | 3.0003 | 1.4222 | | | |
| Sleep quality | EM cluster | Value | Examples | Average | Std-dev | Variance decomposition | Segment-1 | 2200 | 3.8932 | 1.0205 | Source | Sum of square | d.f. | Segment-2 | 2960 | 4.2929 | 0.7456 | BSS | 14783.2298 | 7 | Segment-3 | 3213 | 2.5387 | 1.3247 | WSS | 30937.2330 | 22498 | Segment-4 | 2827 | 1.6314 | 0.6834 | TSS | 45720.4628 | 22505 | Segment-5 | 2963 | 3.2973 | 1.3514 | Significance level | Segment-6 | 1848 | 2.0568 | 1.0512 | Statistics | Value | Proba | Segment-7 | 3205 | 3.2424 | 1.3756 | Fisher’s F | 1535.796704 | ≤0.001 | Segment-8 | 3290 | 2.9483 | 1.4094 | | | | All | 22506 | 3.0083 | 1.4253 | | | |
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