Data mining for selective visualization of large spatial datasets

Shashi Shekhar, Chang Tien Lu, Pusheng Zhang, Rulin Liu

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Data mining is the process of extracting implicit, valuable, and interesting information from large sets of data. Visualization is the process of visually exploring data for pattern and trend analysis, and it is a common method of browsing spatial datasets to look for patterns. However, the growing volume of spatial datasets make it difficult for humans to browse such datasets in their entirety, and data mining algorithms are needed to filter out large uninteresting parts of spatial datasets. We construct a web-based visualization software package for observing the summarization of spatial patterns and temporal trends. We also present data mining algorithms for filtering out vast parts of datasets for spatial outlier patterns. The algorithms were implemented and tested with a real-world set of Minneapolis-St. Paul(Twin Cities) traffic data.

Original languageEnglish (US)
Pages (from-to)41-48
Number of pages8
JournalProceedings of the International Conference on Tools with Artificial Intelligence
StatePublished - Jan 1 2002


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