Tag: sequence mining

Notes: Visualizing sequential patterns for text mining

Reference:

Wong, P. C., Cowley, W., Foote, H., Jurrus, E., & Thomas, J. (2000). Visualizing sequential patterns for text mining. In Information Visualization, 2000. InfoVis 2000. IEEE Symposium on (pp. 105-111). IEEE.

Background:

  • Mining Sequential patterns aims to identify recurring patterns from data over a period of time.
  • A pattern is a finite series of elements from the same domain A -> B -> C -> D
  • Each pattern has a minimum ‘support’ value which indicates the percentage of pattern occurrence. (E.g. 90% of people who did this process, did the second process, followed by the third process)
  • Sequential pattern vs association rule:
    • Sequential pattern – studies ordering/arrangement of elements E.g. A -> B -> C -> D
    • Association rule – studies togetherness E.g. A+B+C -> D

Purpose:

  • Presenting a visual data mining system that combines pattern discovery and visualizations.

Method:

Datasets:

Open source corpus containing 1170 news articles from 1991 to 1997 and harvested news of 1990 from TREC5 distribution.

Pre-processing:

  1. Topic Extraction: Identifies the topic in documents based on the co-occurrence of words. Words separated by white space evaluated – stemming done, prepositions, pronouns, adjectives, and gerunds ignored.
  2. Multiresolution binning: Bins articles with the same timestamp (E.g. Binning by day, week, month, year)

Discovery of sequential patterns by Visualization:

  • Plotting topics/ topic combinations over time.
  • Strength: Can quickly view overall patterns and individual occurrence of events.
  • Weakness: No knowledge on exact connections that make up the pattern and statistical support on the individual patterns.

Discovery of sequential patterns by Data mining:

  • Building patterns on n-ary tree with elements as nodes.
  • Patterns are valid if the support value is greater than threshold.
  • A sample pattern mining from given input data is given in Figure 2 of the paper.
  • Strength: Provides accurate statistical (support) values for all weak and strong patterns.
  • Weakness: Loses temporal and locality information, large number of patterns produced in text format making human interpretation harder.

Visual Data Mining system:

visual-data-mining

  • Combining visualization and data mining to compensate each others’ weaknesses (Refer Figure 4 & 5 in the paper to see the pattern visualizations).
  • Binning resolution can be changed to see different patterns based on day, week, month, year etc.
  • Patterns associated to a particular topic can be picked.

Result/Discussion:

  • Strength of pattern is not easily identifiable from the visualization without statistical measures. Pattern mining gets enhanced by graphical encoding with spatial and temporal information.
  • Knowledge discovery by humans is aided by combining statistical data mining and visualization.

Future Work:

  • Handling larger data sets using secondary memory support and improve display.
  • Integrating more techniques like association rules into visual data mining environment.