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Fast mining and forecasting of complex time-stamped events

Published: 12 August 2012 Publication History

Abstract

Given huge collections of time-evolving events such as web-click logs, which consist of multiple attributes (e.g., URL, userID, times- tamp), how do we find patterns and trends? How do we go about capturing daily patterns and forecasting future events? We need two properties: (a) effectiveness, that is, the patterns should help us understand the data, discover groups, and enable forecasting, and (b) scalability, that is, the method should be linear with the data size. We introduce TriMine, which performs three-way mining for all three attributes, namely, URLs, users, and time. Specifically TriMine discovers hidden topics, groups of URLs, and groups of users, simultaneously. Thanks to its concise but effective summarization, it makes it possible to accomplish the most challenging and important task, namely, to forecast future events. Extensive experiments on real datasets demonstrate that TriMine discovers meaningful topics and makes long-range forecasts, which are notoriously difficult to achieve. In fact, TriMine consistently outperforms the best state-of-the-art existing methods in terms of accuracy and execution speed (up to 74x faster).

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    cover image ACM Conferences
    KDD '12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
    August 2012
    1616 pages
    ISBN:9781450314626
    DOI:10.1145/2339530
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    Published: 12 August 2012

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    Author Tags

    1. forecasting
    2. tensor analysis
    3. time-stamped events
    4. topic model

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    • (2024)From Context to Forecast: Ontology-Based Data Integration and AI for Events PredictionAdvanced Information Networking and Applications10.1007/978-3-031-57870-0_31(349-361)Online publication date: 10-Apr-2024
    • (2023)Maximizing the Spread of Effective Information in Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.313878335:4(4062-4076)Online publication date: 1-Apr-2023
    • (2023)Modeling Hierarchical Seasonality Through Low-Rank Tensor Decompositions in Time Series AnalysisIEEE Access10.1109/ACCESS.2023.329859711(85770-85784)Online publication date: 2023
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