A Fuzzy Association Rules Mining Analysis of the Influencing Factors on the Failure of oBike in Taiwan
Abstract
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Abstract
1. Introduction
- What kinds of factors caused oBike’s failure according to public opinion on the web?
- Does public opinion on the web envisage the failure of oBike and offer any advice on the bike-sharing system?
2. Literature Review
3. Methodology
3.1. Research Process
3.2. Fuzzy Set Theory
3.3. Fuzzy Association Mining
3.3.1. Fuzzy A priori Algorithm
- Step 1:
- Transform the quantitative values (vj(i)) of each transaction data (D(i)) for each attribute (Aj) into a fuzzy set (fj(i)) represented as , where Rjm is the n-th fuzzy region of the attribute Aj, fjki is vj(i)’s fuzzy membership value in the region Rjk, and l(=|Aj|) is the number of fuzzy regions of Aj.
- Step 2:
- Calculate the number (Z) of each attribute region (linguistic term) Rjk in the transaction data, .
- Step 3:
- Collect each attribute region to form the candidate set Cl.
- Step 4:
- Check whether the Zjk of each Rjk is larger than or equal to the predefined minimum support value α. Rjk would be included in the set of 1 itemsets (L1).
- Step 5:
- If L1 is not null, follow the next step. If not, exit the algorithm.
- Step 6:
- Set r = 1; r represents the number stayed into large itemsets. Then, join the large itemsets Lr to generate the candidate set Cr+1 in a way similar to that in the a priori algorithm except that two regions (linguistic terms) belonging to the same attribute cannot simultaneously exist in an itemset in Cr+1.
- Step 7:
- Calculate the fuzzy values of each transaction data in each newly formed (r + 1) itemset s, with itemsets (s1, s2, …, sr + 1) in Cr+1, and then calculate the count of s in the transactions. If the count of s is larger than or equal to the predetermined minimum support value α, put s into Lr+1.
- Step 8:
- If Lr+1 is null, then take the next step; otherwise, set r = r + 1 and repeat steps 6 and 7.
- Step 9:
- Collect the itemsets.
- Step 10:
- Construct association rules for each large q-itemset s with item (s1, s2, …, sq), q ≥ 2.
- Step 11:
- List the output of the association rules with confidence values larger than or equal to the predefined threshold λ.
3.3.2. Genetic Fuzzy A priori Algorithm
- Step 1:
- Generate a random population of P individuals. Each individual is a set of membership functions for all m items.
- Step 2:
- Encode each set of membership functions into a string representation.
- Step 3:
- Calculate the fitness value of each chromosome.
- Step 4:
- Execute the crossover operations on the whole population.
- Step 5:
- Execute the mutation operations on the whole population.
- Step 6:
- Using the selection criteria to choose individuals of the next generation.
- Step 7:
- If the termination condition is not satisfied, go to step 3; otherwise, go to the next step.
- Step 8:
- Obtain the set of memberships with the highest fitness value.
3.3.3. Genetic Fuzzy A priori DC Algorithm
3.4. Data Description
4. Results
4.1. Text Mining Results
4.2. Fuzzy Association Rules Mining Results
Genetic Fuzzy Apriori Algorithm Results
5. Discussion
- According to the fuzzy association rules mining results, the study found the genetic fuzzy a priori algorithm to have a better performance in terms of obtaining the fuzzy association rules with the higher average lift ratio. The study concluded the first 18 fuzzy association rules with the same consequent “user”. This meant the oBike-related web posts involved the “user” concept.
- The study also focused on the top five fuzzy association rules with the highest lift ratio (3.424), as shown in Table 4, as well as the antecedents of the top 5 rules. The antecedents of the top 5 fuzzy association rules included “deposits”, “management”, “service provider”, “oBike”, “Ubike”, and “parking”. It can be concluded that oBike-related web posts concerned the deposits, management, service provider, oBike, Ubike (a brand name of the public bike-sharing service provider) and parking, and that these keywords affected the frequency of the keyword “user”. This indicated that public opinion concerned the users’ viewpoints on the key elements of the oBike service: deposits, management, service provider, oBike’s brand image, oBike’s competitor “Ubike”, and parking. Because the related articles on the “mobile 01” social website discussion boards were filled with negative evaluation of oBike, the text mining and fuzzy association rules mining results reflect the users’ experience and the defects of the oBike system, such as its management problem.
- Since the fuzzy association mining rules results did not clearly indicate the actual time of the association rules, this study attempted to compare the keyword frequency and positive cut-off values, as shown in Table 4. This study chose the time points of the consequent “user” in the higher range of the keyword frequency. This study also combined the top five fuzzy association rules in order to obtain the results showcasing that the consequent “user” was in a higher range of frequency in September, October, and December 2017. As for the antecedent in these months, in September 2017, “management” and “parking” were also in the higher range of keyword frequency. This implies that the users’ higher level of concern about management and parking was the main focus at that time. In October 2017, “deposits”, “management”, “service provider”, “oBike”, “Ubike”, and “parking” were in the lower range of the keyword’s frequencies. This means that none of these factors had a larger impact on the keyword frequency of “user”. In December 2017, “oBike” was in the higher range of keyword frequency. This meant that web post users’ focus on “oBike” had a positive impact on the keyword frequency of “user”, and vice versa.
- This study got the fuzzy association rules results of the oBike-related “Mobile01” web post keywords and obtained the influencing factors of oBike’s failure as follows:
- Management and parking problems: According to the combined top five fuzzy association rules analysis, the users’ higher-level concern about management and parking was the main focus in September 2017. According to oBike’s business history in Taiwan, as mentioned in the introduction section, oBike began its business in Taiwan in 2017. However, “Mobile01” web posts related to oBike pointed out the management and parking problems. As was mentioned in the introduction part, oBike’s business expansion was fast in Taiwan, but its business soon terminated in 2018. It can be induced that the “Mobile01” web posts related to the oBike reflected the oBike’s problem before it ceased operation.
- Competitor’s impact: The results indicate that web post comments related to Ubike positively affected the frequency of the keyword “user”. This implies that the successful business model of Ubike influences users’ evaluation and made it difficult for oBike to gain the market share in the bike-sharing market. The results also confirm the competitor’s success.
- In terms of previous research about the bike-sharing system problem in Mainland China, Wu and Lei (2019) concluded that the bike-sharing problem in Mainland China included the deposits, management, and sustainability problems [45]. However, the results of this study indicated the main problems of oBike’s failure in Taiwan included management, parking, deposits, and the competitor’s impact. This implies the business operation of oBike in Taiwan encountered the same problems: management, parking, and deposits. However, the fuzzy association rules mining results in this study point out the consequences of the top five rules are “users”, and the competitor’s impact is also the factor for oBike’s failure in Taiwan. This means that oBike’s failure in Taiwan related to the management problem and its negative impact on users.
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Keywords | Chinese Word or Explanation |
---|---|
oBike | a brand name of bike-sharing company |
share | gongxiang |
deposits | yajin |
government | zhengfu |
management | guanli |
Ubike | a brand name of public bike-sharing company |
service provider | yezhe |
parking | tingche |
user | shiyongzhe |
Keywords | Maximum | Minimum | Average | Standard Error |
---|---|---|---|---|
oBike | 0.834 | 0 | 0.337 | 0.289 |
share | 1.182 | 0 | 0.419 | 0.465 |
deposits | 0.941 | 0 | 0.127 | 0.268 |
government | 0.907 | 0 | 0.119 | 0.230 |
management | 0.89 | 0 | 0.154 | 0.245 |
Ubike | 0.498 | 0 | 0.093 | 0.124 |
service provider | 1.78 | 0 | 0.279 | 0.471 |
parking | 1.09 | 0 | 0.349 | 0.400 |
user | 1.366 | 0 | 0.226 | 0.393 |
Algorithm | #Rules | Support | Lift | Confidence | CF | Imbalance |
---|---|---|---|---|---|---|
Fuzzy a priori | 6786 | 0.21 | 1.16 | 0.93 | 0.59 | 0.70 |
Genetic fuzzy a priori | 165 | 0.13 | 2.35 | 0.92 | 0.86 | 0.60 |
Genetic fuzzy a priori DC | 1013 | 0.14 | 1.39 | 0.91 | 0.73 | 0.49 |
LHS 1 | RHS 1 | Support | Confidence | Lift | CF | Imbalance |
---|---|---|---|---|---|---|
deposits = −0.17;0.38;0.94, management = −0.05;0.41;0.88, service provider = −0.09;1.02;2.13 | user = −0.11;0.73;1.58 | 0.114 | 1.00 | 3.424 | 1.00 | 0.607 |
deposits = −0.17;0.38;0.94, Obike = −0.03;0.51;0.76, management = −0.05;0.41;0.88, service provider = −0.09;1.02;2.13 | user = −0.11;0.73;1.58 | 0.111 | 1.00 | 3.424 | 1.00 | 0.617 |
Ubike = −0.09;0.28;0.66, Obike = −0.03;0.51;0.76, management = −0.05;0.41;0.88, parking = −0.01;0.63;1.27 | user = −0.11;0.73;1.58 | 0.115 | 1.00 | 3.424 | 1.00 | 0.603 |
deposits = −0.17;0.38;0.94, Ubike = −0.09;0.28;0.66, management = −0.05;0.41;0.88, service provider = −0.09;1.02;2.13 | user = −0.11;0.73;1.58 | 0.114 | 1.00 | 3.424 | 1.00 | 0.607 |
deposits = −0.17;0.38;0.94, Ubike = −0.09;0.28;0.66, Obike = −0.03;0.51;0.76, management = −0.05;0.41;0.88, service provider = −0.09;1.02;2.13 | user = −0.11;0.73;1.58 | 0.111 | 1.00 | 3.424 | 1.00 | 0.617 |
Time | Antecedent 1 | Consequent 1 |
---|---|---|
Sep 2017 | deposits(−), management(+), service provider(−), Obike(−), Ubike(−), parking(+) | user (+) |
Oct 2017 | deposits(−), management(−), service provider(−), Obike(−), Ubike(−), parking(−) | user (+) |
Dec 2017 | deposits(−), management(−), service provider(−), Obike(+), Ubike(−), parking(−) | user (+) |
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Wu, S. A Fuzzy Association Rules Mining Analysis of the Influencing Factors on the Failure of oBike in Taiwan. Mathematics 2020, 8, 1908. https://doi.org/10.3390/math8111908
Wu S. A Fuzzy Association Rules Mining Analysis of the Influencing Factors on the Failure of oBike in Taiwan. Mathematics. 2020; 8(11):1908. https://doi.org/10.3390/math8111908
Chicago/Turabian StyleWu, Shianghau. 2020. "A Fuzzy Association Rules Mining Analysis of the Influencing Factors on the Failure of oBike in Taiwan" Mathematics 8, no. 11: 1908. https://doi.org/10.3390/math8111908
APA StyleWu, S. (2020). A Fuzzy Association Rules Mining Analysis of the Influencing Factors on the Failure of oBike in Taiwan. Mathematics, 8(11), 1908. https://doi.org/10.3390/math8111908