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Mining Top- k Useful Negative Sequential Patterns via Learning.

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    Summary
    This summary is machine-generated.

    This study introduces Topk-NSP+, an efficient algorithm for mining negative sequential patterns (NSPs). It effectively identifies and selects useful NSPs, addressing key challenges in behavior informatics.

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    Area of Science:

    • Behavior informatics
    • Data mining
    • Sequential pattern mining

    Background:

    • Negative sequential patterns (NSPs) are crucial in behavior informatics, often providing more insight than positive sequential patterns (PSPs).
    • Current NSP mining methods face challenges in controlling the number of mined patterns, selecting useful patterns, and managing computational costs.

    Purpose of the Study:

    • To develop an efficient algorithm for mining a specified number of top negative sequential patterns (NSPs).
    • To enhance the selection of useful NSPs by incorporating user preferences and interestingness metrics.
    • To reduce the computational complexity associated with NSP mining.

    Main Methods:

    • Proposed the Topk-NSP algorithm to mine top-k NSPs based on top-k PSPs.
    • Introduced optimization strategies including weighted support (wsup) considering user preferences (wP, wN) and an interestingness metric.
    • Developed Topk-NSP+ with a pruning strategy to improve efficiency and scalability.

    Main Results:

    • Topk-NSP+ successfully mines the top-k most frequent and useful NSPs.
    • Experimental results demonstrate significant efficiency in terms of computational cost and scalability.
    • The algorithm effectively addresses the challenges of NSP mining.

    Conclusions:

    • Topk-NSP+ is the first algorithm capable of mining top-k useful NSPs.
    • The proposed method offers an efficient and scalable solution for negative sequential pattern mining.
    • This advancement has implications for behavior informatics applications requiring insightful pattern discovery.