Efficient web usage mining process for sequential patterns

ABSTRACT

The tremendous growth in volume of web usage data results in the boost of web mining research with focus on discovering potentially useful knowledge from web usage data.

This paper presents a new web usage mining process for finding sequential patterns in web usage data which can be used for predicting the possible next move in browsing sessions for web personalization. This process consists of three main stages: preprocessing web access sequences from the web server log, mining preprocessed web log access sequences by a tree-based algorithm, and predicting web access sequences by using a dynamic clustering-based model. It is designed based on the integration of the dynamic clustering-based Markov model with the Pre-Order Linked WAP-Tree Mining (PLWAP) algorithm to enhance mining performance. The proposed mining process is verified by experiments with promising results.

References

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Index Terms

• Human-centered computing

  • Human computer interaction (HCI)

• Information systems

  • Information retrieval

    • Evaluation of retrieval results

  • Information systems applications

    • Data mining