Multi-dimensional clustering in user profiling

Cufoglu, A. 2012. Multi-dimensional clustering in user profiling. PhD thesis University of Westminster School of Electronics and Computer Science

TitleMulti-dimensional clustering in user profiling
TypePhD thesis
AuthorsCufoglu, A.
Abstract

User profiling has attracted an enormous number of technological methods and

applications. With the increasing amount of products and services, user profiling

has created opportunities to catch the attention of the user as well as achieving

high user satisfaction. To provide the user what she/he wants, when and how,

depends largely on understanding them. The user profile is the representation of

the user and holds the information about the user. These profiles are the

outcome of the user profiling.

Personalization is the adaptation of the services to meet the user’s needs and

expectations. Therefore, the knowledge about the user leads to a personalized

user experience. In user profiling applications the major challenge is to build and

handle user profiles. In the literature there are two main user profiling methods,

collaborative and the content-based. Apart from these traditional profiling

methods, a number of classification and clustering algorithms have been used

to classify user related information to create user profiles. However, the profiling,

achieved through these works, is lacking in terms of accuracy. This is because,

all information within the profile has the same influence during the profiling even

though some are irrelevant user information.

In this thesis, a primary aim is to provide an insight into the concept of user

profiling. For this purpose a comprehensive background study of the literature

was conducted and summarized in this thesis. Furthermore, existing user

profiling methods as well as the classification and clustering algorithms were investigated. Being one of the objectives of this study, the use of these

algorithms for user profiling was examined. A number of classification and

clustering algorithms, such as Bayesian Networks (BN) and Decision Trees

(DTs) have been simulated using user profiles and their classification accuracy

performances were evaluated. Additionally, a novel clustering algorithm for the

user profiling, namely Multi-Dimensional Clustering (MDC), has been proposed.

The MDC is a modified version of the Instance Based Learner (IBL) algorithm.

In IBL every feature has an equal effect on the classification regardless of their

relevance. MDC differs from the IBL by assigning weights to feature values to

distinguish the effect of the features on clustering. Existing feature weighing

methods, for instance Cross Category Feature (CCF), has also been

investigated. In this thesis, three feature value weighting methods have been

proposed for the MDC. These methods are; MDC weight method by Cross

Clustering (MDC-CC), MDC weight method by Balanced Clustering (MDC-BC)

and MDC weight method by changing the Lower-limit to Zero (MDC-LZ). All of

these weighted MDC algorithms have been tested and evaluated. Additional

simulations were carried out with existing weighted and non-weighted IBL

algorithms (i.e. K-Star and Locally Weighted Learning (LWL)) in order to

demonstrate the performance of the proposed methods. Furthermore, a real life scenario is implemented to show how the MDC can be used for the user

profiling to improve personalized service provisioning in mobile environments.

The experiments presented in this thesis were conducted by using user profile

datasets that reflect the user’s personal information, preferences and interests.

The simulations with existing classification and clustering algorithms (e.g. Bayesian Networks (BN), Naïve Bayesian (NB), Lazy learning of Bayesian

Rules (LBR), Iterative Dichotomister 3 (Id3)) were performed on the WEKA

(version 3.5.7) machine learning platform. WEKA serves as a workbench to

work with a collection of popular learning schemes implemented in JAVA. In

addition, the MDC-CC, MDC-BC and MDC-LZ have been implemented on

NetBeans IDE 6.1 Beta as a JAVA application and MATLAB. Finally, the real life

scenario is implemented as a Java Mobile Application (Java ME) on NetBeans

IDE 7.1. All simulation results were evaluated based on the error rate and

accuracy.

Year2012
FileAyse_CUFOGLU.pdf
Publication dates
Completed2012

Related outputs

Testing and Analysis of Activities of Daily Living Data with Machine Learning Algorithms
Cufoglu, A. and Coskun, A. 2016. Testing and Analysis of Activities of Daily Living Data with Machine Learning Algorithms. International Journal of Advanced Computer Science & Applications . 7 (3), pp. 436-441.

Weighted instance based learner (WIBL) for user profiling
Cufoglu, A., Lohi, M. and Everiss, C. 2012. Weighted instance based learner (WIBL) for user profiling. in: 10th IEEE Jubilee International Symposium on Applied Machine Intelligence and Informatics, 26th -28th January 2012, Herl’any, Slovakia IEEE . pp. 201-205

A comparative study of selected classifiers with classification accuracy in user profiling
Cufoglu, A., Lohi, M. and Madani, K. 2009. A comparative study of selected classifiers with classification accuracy in user profiling. in: Burgin, M., Chowdhury, M.H., Ham, C.H., Ludwig, S., Su, W. and Yenduri, S. (ed.) 2009 World Congress on Computer Science and Information Engineering (CSIE 2009), March 31 - April 2, 2009, Los Angeles/Anaheim, USA IEEE . pp. 708-712

A comparative study of selected classification accuracy in user profiling
Cufoglu, A., Lohi, M. and Madani, K. 2008. A comparative study of selected classification accuracy in user profiling. in: ICMLA '08: The Seventh International Conference on Machine Learning and Applications; San Diego, CA, USA December 11-13, 2008 IEEE . pp. 787-791

Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules(LBR) and Instance-Based Learner (IB1) - comparative study
Cufoglu, A., Lohi, M. and Madani, K. 2008. Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules(LBR) and Instance-Based Learner (IB1) - comparative study. in: Fahmy, H.M.A., Wahba, A.M., El-Kharashi, M.W., El-Din, A.M.B., Sobh, M.A. and Tahar, M. (ed.) IEEE International Conference on Computer Engineering and Systems (ICCES'08), Cairo, Egypt, 25 - 27 Nov 2008 IEEE . pp. 210-215

Permalink - https://westminsterresearch.westminster.ac.uk/item/8z6wq/multi-dimensional-clustering-in-user-profiling


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