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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Mining multi-faceted data

Wan, Chang, 萬暢 January 2013 (has links)
Multi-faceted data contains different types of objects and relationships between them. With rapid growth of web-based services, multi-faceted data are increasing (e.g. Flickr, Yago, IMDB), which offers us richer information to infer users’ preferences and provide them better services. In this study, we look at two types of multi-faceted data: social tagging system and heterogeneous information network and how to improve service such as resources retrieving and classification on them. In social tagging systems, resources such as images and videos are annotated with descriptive words called tags. It has been shown that tag-based resource searching and retrieval is much more effective than content-based retrieval. With the advances in mobile technology, many resources are also geo-tagged with location information. We observe that a traditional tag (word) can carry different semantics at different locations. We study how location information can be used to help distinguish the different semantics of a resource’s tags and thus to improve retrieval accuracy. Given a search query, we propose a location-partitioning method that partitions all locations into regions such that the user query carries distinguishing semantics in each region. Based on the identified regions, we utilize location information in estimating the ranking scores of resources for the given query. These ranking scores are learned using the Bayesian Personalized Ranking (BPR) framework. Two algorithms, namely, LTD and LPITF, which apply Tucker Decomposition and Pairwise Interaction Tensor Factorization, respectively for modeling the ranking score tensor are proposed. Through experiments on real datasets, we show that LTD and LPITF outperform other tag-based resource retrieval methods. A heterogeneous information network (HIN) is used to model objects of different types and their relationships. Meta-paths are sequences of object types. They are used to represent complex relationships between objects beyond what links in a homogeneous network capture. We study the problem of classifying objects in an HIN. We propose class-level meta-paths and study how they can be used to (1) build more accurate classifiers and (2) improve active learning in identifying objects for which training labels should be obtained. We show that class-level meta-paths and object classification exhibit interesting synergy. Our experimental results show that the use of class-level meta-paths results in very effective active learning and good classification performance in HINs. / published_or_final_version / Computer Science / Master / Master of Philosophy
2

A study on quantitative association rules

王漣, Wang, Lian. January 1999 (has links)
published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy
3

Pattern discovery from spatiotemporal data

Cao, Huiping., 曹會萍. January 2006 (has links)
published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy
4

Mining order-preserving submatrices from data with repeated measurements

Zhu, Xinjie., 朱信杰. January 2010 (has links)
published_or_final_version / Computer Science / Master / Master of Philosophy
5

Gaining Strategic Advantage through Bibliomining: Data Mining for Management Decisions in Corporate, Special, Digital, and Traditional Libraries

Nicholson, Scott, Stanton, Jeffrey M. January 2003 (has links)
Library and information services in corporations, schools, universities, and communities capture information about their users, circulation history, resources in the collection, and search patterns (Koenig, 1985). Unfortunately, few libraries have taken advantage of these data as a way to improve customer service, manage acquisition budgets, or influence strategic decision-making about uses of information in their organizations. In this chapter, we present a global view of the data generated in libraries and the variety of decisions that those data can inform. We describe ways in which library and information managers can use data mining in their libraries, i.e. bibliomining, to understand patterns of behavior among library users and staff members and patterns of information resource use throughout the institution. The chapter examines data sources and possible applications of data mining techniques and explores the legal and ethical implications of data mining in libraries.
6

A model-based approach for distributed data mining

Zhang, Xiaofeng 01 January 2007 (has links)
No description available.
7

A Novel Method to Intelligently Mine Social Media to Assess Consumer Sentiment of Pharmaceutical Drugs

Akay, Altug January 2017 (has links)
This thesis focuses on the development of novel data mining techniques that convert user interactions in social media networks into readable data that would benefit users, companies, and governments. The readable data can either warn of dangerous side effects of pharmaceutical drugs or improve intervention strategies. A weighted model enabled us to represent user activity in the network, that allowed us to reflect user sentiment of a pharmaceutical drug and/or service. The result is an accurate representation of user sentiment. This approach, when modified for specific diseases, drugs, and services, can enable rapid user feedback that can be converted into rapid responses from consumers to industry and government to withdraw possibly dangerous drugs and services from the market or improve said drugs and services. Our approach monitors social media networks in real-time, enabling government and industry to rapidly respond to consumer sentiment of pharmaceutical drugs and services. / <p>QC 20170314</p>
8

Investigation of discovering rules from data.

January 2000 (has links)
by Ng, King Kwok. / Thesis submitted in: December 1999. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 99-104). / Abstracts in English and Chinese. / Acknowledgments --- p.ii / Abstract --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining and Rule Discovery --- p.1 / Chapter 1.1.1 --- Association Rule --- p.3 / Chapter 1.1.2 --- Sequential Pattern --- p.4 / Chapter 1.1.3 --- Dependence Rule --- p.6 / Chapter 1.2 --- Association Rule Mining --- p.8 / Chapter 1.3 --- Contributions --- p.9 / Chapter 1.4 --- Outline of the Thesis --- p.10 / Chapter 2 --- Related Work on Association Rule Mining --- p.11 / Chapter 2.1 --- Batch Algorithms --- p.11 / Chapter 2.1.1 --- The Apriori Algorithm --- p.11 / Chapter 2.1.2 --- The DIC Algorithm --- p.13 / Chapter 2.1.3 --- The Partition Algorithm --- p.15 / Chapter 2.1.4 --- The Sampling Algorithm --- p.15 / Chapter 2.2 --- Incremental Association Rule Mining --- p.16 / Chapter 2.2.1 --- The FUP Algorithm --- p.17 / Chapter 2.2.2 --- The FUP2 Algorithm --- p.18 / Chapter 2.2.3 --- The FUP* Algorithm --- p.19 / Chapter 2.2.4 --- The Negative Border Method --- p.20 / Chapter 2.2.5 --- Limitations of Existing Incremental Association Rule Mining Algorithms --- p.21 / Chapter 3 --- A New Incremental Association Rule Mining Approach --- p.23 / Chapter 3.1 --- Outline for the Proposed Approach --- p.23 / Chapter 3.2 --- Our New Approach --- p.26 / Chapter 3.2.1 --- The IDIC_M Algorithm --- p.26 / Chapter 3.2.2 --- A Variant Algorithm: The IDIC_S Algorithm --- p.29 / Chapter 3.3 --- Performance Evaluation of Our Approach --- p.30 / Chapter 3.3.1 --- Experimental Results for Algorithm IDIC_M --- p.30 / Chapter 3.3.2 --- Experimental Results for Algorithm IDIC_S --- p.35 / Chapter 3.4 --- Discussion --- p.39 / Chapter 4 --- Related Work on Multiple_Level AR and Belief-Driven Mining --- p.41 / Chapter 4.1 --- Background on Multiple_Level Association Rules --- p.41 / Chapter 4.2 --- Related Work on Multiple-Level Association Rules --- p.42 / Chapter 4.2.1 --- The Basic Algorithm --- p.42 / Chapter 4.2.2 --- The Cumulate Algorithm --- p.44 / Chapter 4.2.3 --- The EstMerge Algorithm --- p.44 / Chapter 4.2.4 --- Using Hierarchy-Information Encoded Transaction Table --- p.45 / Chapter 4.3 --- Background on Rule Mining in the Presence of User Belief --- p.46 / Chapter 4.4 --- Related Work on Rule Mining in the Presence of User Belief --- p.47 / Chapter 4.4.1 --- Post-Analysis of Learned Rules --- p.47 / Chapter 4.4.2 --- Using General Impressions to Analyze Discovered Classification Rules --- p.49 / Chapter 4.4.3 --- A Belief-Driven Method for Discovering Unexpected Patterns --- p.50 / Chapter 4.4.4 --- Constraint-Based Rule Mining --- p.51 / Chapter 4.5 --- Limitations of Existing Approaches --- p.52 / Chapter 5 --- Multiple-Level Association Rules Mining in the Presence of User Belief --- p.54 / Chapter 5.1 --- User Belief Under Taxonomy --- p.55 / Chapter 5.2 --- Formal Definition of Rule Interestingness --- p.57 / Chapter 5.3 --- The MARUB_E Mining Algorithm --- p.61 / Chapter 6 --- Experiments on MARUB_E --- p.64 / Chapter 6.1 --- Preliminary Experiments --- p.64 / Chapter 6.2 --- Experiments on Synthetic Data --- p.68 / Chapter 6.3 --- Experiments on Real Data --- p.71 / Chapter 7 --- Dealing with Vague Belief of User --- p.76 / Chapter 7.1 --- User Belief Under Taxonomy --- p.76 / Chapter 7.2 --- Relationship with Constraint-Based Rule Mining --- p.79 / Chapter 7.3 --- Formal Definition of Rule Interestingness --- p.79 / Chapter 7.4 --- The MARUB_V Mining Algorithm --- p.81 / Chapter 8 --- Experiments on MARUB_V --- p.84 / Chapter 8.1 --- Preliminary Experiments --- p.84 / Chapter 8.1.1 --- Experiments on Synthetic Data --- p.87 / Chapter 8.1.2 --- Experiments on Real Data --- p.93 / Chapter 9 --- Conclusions and Future Work --- p.96 / Chapter 9.1 --- Conclusions --- p.95 / Chapter 9.2 --- Future Work --- p.97
9

Techniques in data mining: decision trees classification and constraint-based itemsets mining.

January 2001 (has links)
Cheung, Yin-ling. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 117-124). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining Techniques --- p.1 / Chapter 1.1.1 --- Classification --- p.1 / Chapter 1.1.2 --- Association Rules Mining --- p.2 / Chapter 1.1.3 --- Estimation --- p.2 / Chapter 1.1.4 --- Prediction --- p.2 / Chapter 1.1.5 --- Clustering --- p.2 / Chapter 1.1.6 --- Description --- p.3 / Chapter 1.2 --- Problem Definition --- p.3 / Chapter 1.3 --- Thesis Organization --- p.4 / Chapter I --- Decision Tree Classifiers --- p.6 / Chapter 2 --- Background --- p.7 / Chapter 2.1 --- Introduction to Classification --- p.7 / Chapter 2.2 --- Classification Using Decision Trees --- p.8 / Chapter 2.2.1 --- Constructing a Decision Tree --- p.10 / Chapter 2.2.2 --- Related Work --- p.11 / Chapter 3 --- Strategies to Enhance the Performance in Building Decision Trees --- p.14 / Chapter 3.1 --- Introduction --- p.15 / Chapter 3.1.1 --- Related Work --- p.15 / Chapter 3.1.2 --- Post-evaluation vs Pre-evaluation of Splitting Points --- p.19 / Chapter 3.2 --- Schemes to Construct Decision Trees --- p.27 / Chapter 3.2.1 --- One-to-many Hashing --- p.27 / Chapter 3.2.2 --- Many-to-one and Horizontal Hashing --- p.28 / Chapter 3.2.3 --- A Scheme using Paired Attribute Lists --- p.29 / Chapter 3.2.4 --- A Scheme using Database Replication --- p.31 / Chapter 3.3 --- Performance Analysis --- p.32 / Chapter 3.4 --- Experimental Results --- p.38 / Chapter 3.4.1 --- Performance --- p.38 / Chapter 3.4.2 --- Test 1 : Smaller Decision Tree --- p.40 / Chapter 3.4.3 --- Test 2: Bigger Decision Tree --- p.44 / Chapter 3.5 --- Conclusion --- p.47 / Chapter II --- Mining Association Rules --- p.48 / Chapter 4 --- Background --- p.49 / Chapter 4.1 --- Definition --- p.49 / Chapter 4.2 --- Association Algorithms --- p.51 / Chapter 4.2.1 --- Apriori-gen --- p.51 / Chapter 4.2.2 --- Partition --- p.53 / Chapter 4.2.3 --- DIC --- p.54 / Chapter 4.2.4 --- FP-tree --- p.54 / Chapter 4.2.5 --- Vertical Data Mining --- p.58 / Chapter 4.3 --- Taxonomies of Association Rules --- p.58 / Chapter 4.3.1 --- Multi-level Association Rules --- p.58 / Chapter 4.3.2 --- Multi-dimensional Association Rules --- p.59 / Chapter 4.3.3 --- Quantitative Association Rules --- p.59 / Chapter 4.3.4 --- Random Sampling --- p.60 / Chapter 4.3.5 --- Constraint-based Association Rules --- p.60 / Chapter 5 --- Mining Association Rules without Support Thresholds --- p.62 / Chapter 5.1 --- Introduction --- p.63 / Chapter 5.1.1 --- Itemset-Loop --- p.66 / Chapter 5.2 --- New Approaches --- p.67 / Chapter 5.2.1 --- "A Build-Once and Mine-Once Approach, BOMO" --- p.68 / Chapter 5.2.2 --- "A Loop-back Approach, LOOPBACK" --- p.74 / Chapter 5.2.3 --- "A Build-Once and Loop-Back Approach, BOLB" --- p.77 / Chapter 5.2.4 --- Discussion --- p.77 / Chapter 5.3 --- Generalization: Varying Thresholds Nk for k-itemsets --- p.78 / Chapter 5.4 --- Performance Evaluation --- p.78 / Chapter 5.4.1 --- Generalization: Varying Nk for k-itemsets --- p.84 / Chapter 5.4.2 --- Non-optimal Thresholds --- p.84 / Chapter 5.4.3 --- "Different Decrease Factors,f" --- p.85 / Chapter 5.5 --- Conclusion --- p.87 / Chapter 6 --- Mining Interesting Itemsets with Item Constraints --- p.88 / Chapter 6.1 --- Introduction --- p.88 / Chapter 6.2 --- Proposed Algorithms --- p.91 / Chapter 6.2.1 --- Single FP-tree Approach --- p.92 / Chapter 6.2.2 --- Double FP-trees Approaches --- p.93 / Chapter 6.3 --- Maximum Support Thresholds --- p.102 / Chapter 6.4 --- Performance Evaluation --- p.103 / Chapter 6.5 --- Conclusion --- p.109 / Chapter 7 --- Conclusion --- p.110 / Chapter A --- Probabilistic Analysis of Hashing Schemes --- p.112 / Chapter B --- Hash Functions --- p.114 / Bibliography --- p.117
10

Concurrent data mining with a large number of users.

January 2004 (has links)
Li Zhiheng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 77-79). / Abstracts in English and Chinese. / Abstract (English) --- p.i / Acknowledgement --- p.iii / Contents --- p.iv / List of Figures --- p.vii / List of Tables --- p.ix / List of Algorithms --- p.x / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Review of frequent itemset mining --- p.1 / Chapter 1.2 --- Data mining proxy serving for large numbers of users --- p.3 / Chapter 1.3 --- Privacy issues on proxy service --- p.4 / Chapter 1.4 --- Organization of the thesis --- p.6 / Chapter 2 --- Frequent itemsets mining --- p.7 / Chapter 2.1 --- Preliminaries --- p.7 / Chapter 2.2 --- Data mining queries --- p.8 / Chapter 2.3 --- A running example --- p.10 / Chapter 3 --- Data Mining Proxy --- p.13 / Chapter 3.1 --- Load data for mining --- p.14 / Chapter 3.2 --- An Overview --- p.16 / Chapter 3.3 --- Tree Operations --- p.16 / Chapter 3.4 --- Data Mining Usages and Observations --- p.18 / Chapter 4 --- Implementation of Proxy --- p.23 / Chapter 4.1 --- Problems in implementation --- p.23 / Chapter 4.2 --- A Coding Scheme --- p.24 / Chapter 4.3 --- On-disk/In-Memory Tree Representations and Mining --- p.27 / Chapter 4.4 --- Tree Operation Implementations --- p.29 / Chapter 4.4.1 --- Tree Projection Operation Implementations: πd2m( )and πm2m( ) --- p.31 / Chapter 4.4.2 --- Tree Merge Operation Implementations: --- p.33 / Chapter 4.4.3 --- Frequent Itemset/Sub-itemset Tree Building Request --- p.37 / Chapter 4.4.4 --- The Tree Projection Operation π and Frequent Super- itemset Tree Building Request --- p.39 / Chapter 5 --- Performance Studies --- p.45 / Chapter 5.1 --- Mining with Different Sizes of Trees in Main Memory --- p.47 / Chapter 5.2 --- Constructing Trees in Main Memory --- p.48 / Chapter 5.3 --- Query Patterns and Number of Queries --- p.50 / Chapter 5.4 --- Testing Sub-itemset Queries with Different Memory Sizes --- p.51 / Chapter 5.5 --- Replacement Strategies --- p.51 / Chapter 6 --- Privacy Preserving in Proxy Service --- p.61 / Chapter 6.1 --- Data Union Regardless Privacy Preserving --- p.61 / Chapter 6.2 --- Secure Data Union --- p.65 / Chapter 6.2.1 --- Secure Multi-party Computation --- p.65 / Chapter 6.2.2 --- Basic Methods of Privacy Preserving in Semi-honest Envi- ronment --- p.67 / Chapter 6.2.3 --- Privacy Preserving On Data Union --- p.70 / Chapter 6.3 --- Discussions --- p.73 / Chapter 7 --- Conclusion --- p.75 / Bibliography --- p.77

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