读Extracting Urban Patterns from Location-based Social Networks
转自http://longriver.me/?p=320spa
这篇文章主要使用LDA topic model 对大众轨迹的pattern(topics)进行挖掘，读了以后，对LDA这种模型的认识又有了些新的认识：
1，是其在处理大量的稀疏数据时表现出来的好的效果
2，对这种几率分布的结果，可操做空间比较大，相比于使用社区发现（community-detection）来讲,社区发现发觉话题和社区结构的方法过硬，缺乏量化的指标
3，对于轨迹挖掘的一些思考，轨迹挖掘，之前使用较多的是频繁模式，顺序频繁模式等，如今用几率的方法，得出的pattern解析性也不错，效果也很好。get
相关文章
- 1. Deep learning-based personality recognition from text posts of online social networks 阅读笔记
- 2. Design Principles from Design Patterns
- 3. ArnetMiner: Extraction and Mining of Academic Social Networks
- 4. Graph Neural Networks for Social Recommendation
- 5. Complex urban dataset with multi-level sensors from highly diverse urban environments
- 6. [NLU] Extracting Symptoms and their Status from Clinical Conversations
- 7. Unsupervised Sentiment Analysis with Signed Social Networks--阅读笔记
- 8. Academic social networks: Modeling, analysis, mining and applications
- 9. 读书笔记一——Urban Computing with Taxicabs
- 10. 《Predict Anchor Links across Social Networks via an Embedding Approach》阅读笔记
- 更多相关文章...
- • RSS 阅读器 - RSS 教程
- • PHP MySQL 读取数据 - PHP教程
- • JDK13 GA发布：5大特性解读
- • 漫谈MySQL的锁机制
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Deep learning-based personality recognition from text posts of online social networks 阅读笔记
- 2. Design Principles from Design Patterns
- 3. ArnetMiner: Extraction and Mining of Academic Social Networks
- 4. Graph Neural Networks for Social Recommendation
- 5. Complex urban dataset with multi-level sensors from highly diverse urban environments
- 6. [NLU] Extracting Symptoms and their Status from Clinical Conversations
- 7. Unsupervised Sentiment Analysis with Signed Social Networks--阅读笔记
- 8. Academic social networks: Modeling, analysis, mining and applications
- 9. 读书笔记一——Urban Computing with Taxicabs
- 10. 《Predict Anchor Links across Social Networks via an Embedding Approach》阅读笔记