English  |  正體中文  |  简体中文  |  Items with full text/Total items : 88284/117783 (75%)
Visitors : 23396077      Online Users : 139
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/109682

    Title: 智慧型手機應用程式使用模式探勘 (II)
    Authors: 徐國偉
    Contributors: 資科系
    Keywords: 智慧型手機應用程式;模式探勘;使用者行為
    Smartphone App;Pattern Mining;User Behavior
    Date: 2015
    Issue Date: 2017-05-17 15:25:04 (UTC+8)
    Abstract: 隨著智慧型手機日益普及,相關研究變得更加重要;智慧型手機應用程式的數量正在快速地增加,而這使得應用程式使用的自由度增加但卻讓相關研究變得更有挑戰性。本計畫是『智慧型手機應用程式使用模式探勘』(102-2221-E-004-013)的延伸,其目的是應用資料探勘技術,從智慧型手機應用程式日誌紀錄當中,挖掘出應用程式的使用模式。在沒有任何假設或監督的情形之下,本計畫研究使用者使用應用程式的組合與順序,並將重點放在組合與順序是如何隨時間變化,目標是應用資料探勘技術去發現使用者的習慣的改變。本計畫參考前一個計畫所使用的真實資料來源,但會使用更大的資料集。本計畫用的原始資料集的維度不高,但資料集內有超過一千個應用程式,而每個應用程式有可以有數個行為(功能)。每個應用程式可以是資訊的消費者和生產者,還可以是其使用者和環境之間的介面(例如,照相和錄音應用程式);每個使用者都可以任意安裝、移除,以及使用應用程式,還可任意在應用程式之間切換,而不用受限於預先設定好的“路徑”(例如,超連結引導網頁之間的切換)。因為這些特性,一般的網頁點擊串流探勘技術無法直接使用於本計畫。本計畫的第一個重點是定義模式,第二個重點是對資料進行前置處理,第三個重點是對處理後的資料做序列模式探勘,第四個重點是分析模式的動態。本報告呈現本計畫截至繳交時之研究成果,而結果將可協助社會科學學者制定研究問題(例如,“為什麼使用者要用這種方式使用這個應用程式?”或是“使用者特質會如何影響使用者他們使用應用程式的行為?”)。本計畫的產出將可推動更多相關於智慧型手機應用程式使用的研究。
    With the growing popularity of smartphones, the relevant studies become more important; the number of smartphone applications (Apps) quickly increases, and this increases the degree of freedom of App usage but makes the relevant studies more challenging. This project is an extension of the project “Pattern Mining for Smartphone Application Usage” (102-2221-E-004-013), while its purpose is to apply data mining techniques to mine App usage patterns from smartphone App 日誌紀錄 data. Without any hypothesis or supervision, this project studies the combinations and sequences of Apps that users use, and it puts the focus on how the combinations or sequences change over time, while
    the goal of this project is to apply data mining techniques to discover the changes of users’ habits. This project refers to the real-world data source used by the previous project, but it uses a larger data set. The dimension of the raw data set used in this project is not high, but there are more than one thousand Apps in the data set and each App could have several activities (functions).Every App can be an information consumer and producer, and it can be in interface between its user and the environment (forexample, recorder and camera Apps); every user can
    arbitrarily install, uninstall, and use Apps, and he or she can arbitrarily switch between Apps without being restricted by the pre-defined “paths” (for example, hyperlinks guide switching between web pages).Due to these characteristics, general web clickstream mining techniques cannot be directly applied to this project. The first point on which this project focused is to define usage pattern; the second point is to perform data pre-processing for the
    data; the third point is to perform sequential pattern mining for the pre-processed data; the fourth point is to analyze dynamics of patterns. This report presents the results of this project as at the time of submission, and the results would be able to assist social scientists in asking research questions (for example, “Why would the users use this App in this way?” or “How do users’ characteristics affect their App usage behaviors?”). The output of this project would be able to drive more studies
    relevant to the use of smartphone applications.
    Relation: MOST 103-2221-E-004-015
    Data Type: report
    Appears in Collections:[資訊科學系] 國科會研究計畫

    Files in This Item:

    File Description SizeFormat
    103-2221-E-004-015.pdf1014KbAdobe PDF163View/Open

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback