Name
Abstract | ||
|---|---|---|
In this paper, we target a similarity search among data supply chains, which plays an essential role in optimizing the supply chain and extending its value. This problem is very challenging for application-oriented data supply chains because the high complexity of the data supply chain makes the computation of similarity extremely complex and inefficient. In this paper, we propose a feature space representation model based on key points, which can extract the key features from the subsequences of the original data supply chain and simplify it into a feature vector form. Then, we formulate the similarity computation of the subsequences based on the multiscale features. Further, we propose an improved hierarchical clustering algorithm for a similarity search over the data supply chains. The main idea is to separate the subsequences into disjoint groups such that each group meets one specific clustering criteria; thus, the cluster containing the query object is the similarity search result. The experimental results show that the proposed approach is both effective and efficient for data supply chain retrieval. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.23919/TST.2017.7889639 | Tsinghua Science and Technology |
Keywords | Field | DocType |
Supply chains,Feature extraction,Time series analysis,Search problems,Algorithm design and analysis,Clustering algorithms,Hidden Markov models | Data mining,Correlation clustering,Computer science,Supply chain,Artificial intelligence,Nearest neighbor search,Machine learning | Conference |
Volume | Issue | ISSN |
22 | 2 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Li | 1 | 5 | 2.09 |
| Hong Luo | 2 | 335 | 31.84 |
| Yan Sun | 3 | 1124 | 119.96 |
| Xinming Li | 4 | 1 | 1.37 |