LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Finding Intensional Knowledge of Distance-Based Outliers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Outlier detection in scatterometer data: neural network approaches
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Discovering unexpected documents in corpora
Knowledge-Based Systems
A comparison of outlier detection algorithms for ITS data
Expert Systems with Applications: An International Journal
Finding key attribute subset in dataset for outlier detection
Knowledge-Based Systems
| Hi-index | 0.00 |
Assessing bidder's tender is very important for an employer in deciding whether to award or not. Marking scheme method is generally used to evaluate the tenders of construction project in China. And the mainly advantage of this method is the application of the expert's personal knowledge and experience which makes the contractor selection more reasonable. However, if the low scores given by the experts are usually omitted when the marking scheme method is applied, the contractor with certain potential risks may win the bid, which may make the project not be executed and completed in accordance with the contract. To overcome these weaknesses, the outlier detection model, one of Knowledge Discovery in Database (KDD) method, based on similar coefficient sum was set up in this paper. In this model, the outliers were detected based on the experts' tender evaluation data of the water projects. Then, the risk factors of contractor's attributes were obtained and the risk early warning was carried out. The proposed model is used in the tender evaluation decision-making and risk early warning of the water projects in Tianjin, China. The empirical survey was also made to verify the proposed model. From the feedback of the questionnaires, the survey showed that the outliers detection model could detect the latent risks effectively. Accordingly, some advice was given to the employers to prevent the potential risks. Finally, the proposed method and model were introduced into a tender evaluation decision-making and risk early warning system (TEDREWS) for convenient and efficient tender evaluation and risk management.