Web Log Data Analysis by Enhanced Fuzzy C Means Clustering

Background/Objectives: To measure occupancy using transition probability matrix as a data analysis method to predict future requirements for web use. From this study, Executives facing business challenges can enhance the decision-making process for management and can be provided quantified evidence.Methods/Statistical analysis: Transition matrix and transition probability matrix are estimated if web users’ webpage use patterns are tied with frequency, using web log data. Occupancy is forecasted based on a Markov chain model.Findings: Data analysis from the perspective of web log-based marketing mostly focuses on increasing traffic and improving transition rates. However, general-purpose tools such as Google Analytics provide diverse web log data. In assumption of independence on users’ page reload, occupancy can be easily estimated through matrix on page reload (transition). As a result, we obtained slightly different results from the usual method that reported only frequency. In particular, rather than making business decisions with the frequency of absolute concepts, we were able to identify the top priority services through the percentage value of relative concepts.Improvements/Applications: The occupancy prediction using transition matrix is about future prediction based on previous information. However, it differs from marketing techniques in that it is estimated based on probability. In addition, it is able to predict more accurately through a probability model.

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Web Log Data Analysis and Mining

Communications in Computer and Information Science - Advanced Computing ◽

10.1007/978-3-642-17881-8_44 ◽

2011 ◽

pp. 459-469 ◽

Cited By ~ 8

Author(s):

L. K. Joshila Grace ◽

V. Maheswari ◽

Dhinaharan Nagamalai

Keyword(s):

Data Analysis ◽

Log Data ◽

Web Log ◽

Log Data Analysis

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Characterizing I/O Optimization Effect Through Holistic Log Data Analysis of Parallel File Systems and Interconnects

Lecture Notes in Computer Science - High Performance Computing ◽

10.1007/978-3-030-59851-8_11 ◽

2020 ◽

pp. 177-190

Author(s):

Yuichi Tsujita ◽

Yoshitaka Furutani ◽

Hajime Hida ◽

Keiji Yamamoto ◽

Atsuya Uno

Keyword(s):

Data Analysis ◽

File Systems ◽

Parallel File Systems ◽

Log Data ◽

Parallel File ◽

Log Data Analysis

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Quality assurance of geometric accuracy based on an electronic portal imaging device and log data analysis for Dynamic WaveArc irradiation

Journal of Applied Clinical Medical Physics ◽

10.1002/acm2.12324 ◽

2018 ◽

Vol 19 (3) ◽

pp. 234-242

Author(s):

Hideaki Hirashima ◽

Yuki Miyabe ◽

Mitsuhiro Nakamura ◽

Nobutaka Mukumoto ◽

Takashi Mizowaki ◽

...

Keyword(s):

Quality Assurance ◽

Data Analysis ◽

Electronic Portal Imaging Device ◽

Geometric Accuracy ◽

Imaging Device ◽

Log Data ◽

Portal Imaging ◽

Electronic Portal Imaging ◽

Log Data Analysis

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A scoping review of the use of log data for evaluating mobile Apps (Preprint)

10.2196/preprints.31407 ◽

2021 ◽

Author(s):

Ane van Schalkwyk ◽

Sara Grobbelaar ◽

Euodia Vermeulen

Keyword(s):

Mixed Methods ◽

Data Analysis ◽

Scoping Review ◽

Mobile Apps ◽

Grey Literature ◽

Research Approach ◽

Log Data ◽

Data Framework ◽

Log Data Analysis ◽

Google Search

BACKGROUND There is a growing trend in the potential benefits and application of log data for the evaluation of mHealth Apps. However, the process by which insights may be derived from log data remains unstructured, resulting in underutilisation of mHealth data. OBJECTIVE We aimed to acquire an understanding of how log data analysis can be used to generate valuable insights in support of realistic evaluations of mobile Apps through a scoping review. This understanding is delineated according to publication trends, associated concepts and characteristics of log data, framework or processes required to develop insights from log data, and how these insights may be utilised towards evaluation of Apps. METHODS The PRISMA-ScR guidelines for a scoping review were followed. The Scopus database, the Journal of Medical Internet Research (JMIR), and grey literature (through a Google search) delivered 105 articles of which 33 articles were retained in the sample for analysis and synthesis. RESULTS A definition for log data is developed from its characteristics and articulated as: anonymous records of users’ real-time interactions with the application, collected objectively or automatically and often accessed from cloud-based storage. Publications for theoretical and empirical work on log data analysis have increased between 2010 and 2021 (100% and 95% respectively). The research approach is distributed between inductive (43%), deductive (30%), and a hybrid approach (27%). Research methods include mixed-methods (73%) and quantitative only (27%), although mixed-methods dominate since 2018. Only 30% of studies articulated the use of a framework or model to perform the log data analysis. Four main focus areas for log data analysis are identified as usability (40%), engagement (15%), effectiveness (15%), and adherence (15%). An average of one year of log data is used for analysis, with an average of three years from the launch of the App to the analysis. Collected indicators include user events or clicks made, specific features of the App, and timestamps of clicks. The main calculated indicators are features used or not used (24/33), frequency (21/33), and duration (18/33). Reporting the calculated indicators per ‘user or user group’ was the most used reference point. CONCLUSIONS Standardised terminology, processes, frameworks, and explicit benchmarks to utilise log data are lacking in literature. Thereby, the need for a conceptual framework that is able to standardise the log analysis of mobile Apps is determined. We provide a summary of concepts towards such a framework. CLINICALTRIAL NA

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Fuzzy Clustering

Advances in Business Information Systems and Analytics - Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics ◽

10.4018/978-1-5225-0997-4.ch003 ◽

2017 ◽

pp. 40-61 ◽

Cited By ~ 1

Author(s):

Mashhour H. Baeshen ◽

Malcolm J. Beynon ◽

Kate L. Daunt

Keyword(s):

Data Analysis ◽

Service Quality ◽

Mobile Phone ◽

Fuzzy Clustering ◽

Data Set ◽

Clustering Techniques ◽

Fuzzy C Means ◽

Fuzzy Environment ◽

External Variables ◽

Fuzzy C Means Clustering

This chapter presents a study of the development of the clustering methodology to data analysis, with particular attention to the analysis from a crisp environment to a fuzzy environment. An applied problem concerning service quality (using SERVQUAL) of mobile phone users, and subsequent loyalty and satisfaction forms the data set to demonstrate the clustering issue. Following details on both the crisp k-means and fuzzy c-means clustering techniques, comparable results from their analysis are shown, on a subset of data, to enable both graphical and statistical elucidation. Fuzzy c-means is then employed on the full SERVQUAL dimensions, and the established results interpreted before tested on external variables, namely the level of loyalty and satisfaction across the different clusters established.

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