Visual, linguistic data mining using Self- Organizing Maps
Dumidu Wijayasekara, Milos Manic · 2012
Data mining methods are becoming vital as the amount and complexity of available data is rapidly growing. Visual data mining methods aim at including a human observer in the loop and leveraging human perception for knowledge extraction. However, for large datasets, the rough knowledge gained via visualization is often times not sufficient. Thus, in such cases data summarization can provide a further insight into the problem at hand. Linguistic descriptors such as linguistic summaries and linguistic rules can be used in data summarization to further increase the understandability of datasets. This paper presents a Visual Linguistic Summarization tool (VLS-SOM) that combines the visual data mining capability of the Self-Organizing Map (SOM) with the understandability of linguistic descriptors. This paper also presents new quality measures for ranking of predictive rules. The presented data mining tool enables users to 1) interactively derive summaries and rules about interesting behaviors of the data visualized though the SOM, 2) visualize linguistic descriptors and visually assess the importance of generated summaries and rules. The data mining tool was tested on two benchmark problems. The tool was helpful in identifying important features of the datasets. The visualization enabled the identification of the most important summaries. For classification, the visualization proved useful in identifying multiple rules that classify the dataset.