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Article
Publication date: 20 December 2017

Kaigang Yi, Tinggui Chen and Guodong Cong

Nowadays, database management system has been applied in library management, and a great number of data about readers’ visiting history to resources have been accumulated…

1166

Abstract

Purpose

Nowadays, database management system has been applied in library management, and a great number of data about readers’ visiting history to resources have been accumulated by libraries. A lot of important information is concealed behind such data. The purpose of this paper is to use a typical data mining (DM) technology named an association rule mining model to find out borrowing rules of readers according to their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase utilization rate of data resources at library.

Design/methodology/approach

Association rule mining algorithm is applied to find out borrowing rules of readers according to their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase utilization rate of data resources at library.

Findings

Through an analysis on record of book borrowing by readers, library manager can recommend books that may be interested by a reader based on historical borrowing records or current book-borrowing records of the reader.

Research limitations/implications

If many different categories of book-borrowing problems are involved, it will result in large length of encoding as well as giant searching space. Therefore, future research work may be considered in the following aspects: introduce clustering method; and apply association rule mining method to procurement of book resources and layout of books.

Practical implications

The paper provides a helpful inspiration for Big Data mining and software development, which will improve their efficiency and insight on users’ behavior and psychology.

Social implications

The paper proposes a framework to help users understand others’ behavior, which will aid them better take part in group and community with more contribution and delightedness.

Originality/value

DM technology has been used to discover information concealed behind Big Data in library; the library personalized recommendation problem has been analyzed and formulated deeply; and a method of improved association rules combined with artificial bee colony algorithm has been presented.

Details

Library Hi Tech, vol. 36 no. 3
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 5 May 2021

Shanshan Wang, Jiahui Xu, Youli Feng, Meiling Peng and Kaijie Ma

This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second…

Abstract

Purpose

This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation.

Design/methodology/approach

The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets.

Findings

The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields.

Research limitations/implications

The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future.

Originality/value

The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.

Details

Information Discovery and Delivery, vol. 50 no. 1
Type: Research Article
ISSN: 2398-6247

Keywords

Article
Publication date: 1 August 2005

Baoyao Zhou, Siu Cheung Hui and Alvis C. M. Fong

With the explosive growth of information available on the World Wide Web, it has become much more difficult to access relevant information from the Web. One possible…

Abstract

With the explosive growth of information available on the World Wide Web, it has become much more difficult to access relevant information from the Web. One possible approach to solve this problem is web personalization. In this paper, we propose a novel WUL (Web Usage Lattice) based mining approach for mining association access pattern rules for personalized web recommendations. The proposed approach aims to mine a reduced set of effective association pattern rules for enhancing the online performance of web recommendations. We have incorporated the proposed approach into a personalized web recommender system known as AWARS. The performance of the proposed approach is evaluated based on the efficiency and the quality. In the efficiency evaluation, we measure the number of generated rules and the runtime for online recommendations. In the quality evaluation, we measure the quality of the recommendation service based on precision, satisfactory and applicability. This paper will discuss the proposed WUL‐based mining approach, and give the performance of the proposed approach in comparison with the Apriori‐based algorithms.

Details

International Journal of Web Information Systems, vol. 1 no. 3
Type: Research Article
ISSN: 1744-0084

Keywords

Open Access
Article
Publication date: 5 April 2022

Burcu Kartal, Mehmet Fatih Sert and Melih Kutlu

This study aims to provide preliminary information to the investor by determining which indices co-movement, with the data mining method.

Abstract

Purpose

This study aims to provide preliminary information to the investor by determining which indices co-movement, with the data mining method.

Design/methodology/approach

In this context, data sets containing daily opening and closing prices between 2001 and 2019 have been created for 11 stock market indexes in the world. The association rule algorithm, one of the data mining techniques, is used in the analysis of the data.

Findings

It is observed that the US stock market indices take part in the highest confidence levels between association rules. The XU100 stock index co-movement with both the European stock market indices and the US stock indices. In addition, the Hang Seng Index (HSI) (Hong Kong) takes part in the association rules of all stock market indices.

Originality/value

The important issue for data sets is that the opening/closing values of the same day or the previous day are taken into account according to the open or closed status of other stock market indices by taking the opening time of the stock exchange index to be created. Therefore, data sets are arranged for each stock market index, separately. As a result of this data set arranging process, it is possible to find out co-movements of the stock market indexes. It is proof that the world stock indices have co-movement, and this continues as a cycle.

Details

Journal of Economics, Finance and Administrative Science, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2077-1886

Keywords

Article
Publication date: 25 February 2020

Wolfram Höpken, Marcel Müller, Matthias Fuchs and Maria Lexhagen

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point…

Abstract

Purpose

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios.

Design/methodology/approach

The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns.

Findings

The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent).

Research limitations/implications

As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour.

Practical implications

From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment.

Originality/value

The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.

摘要 研究目的

本论文旨在分析图片分享平台Flickr对截取游客空间动线信息和景点(POI)游览行为的适用性, 并且对比最知名的几种聚类分析手段, 以确定不同情况下的POI。

研究设计/方法/途径

本论文首先从Flickr上摘录下图片大数据, 比如上传时间、地点、用户等。其次, 本论文使用DBSCAN和k-means聚类分析参数来将上传图片分配给POI隐性变量。最后, 本论文采用关联规则挖掘分析(FP-growth参数)和序列样式勘探分析(GSP参数)以确认游客行为模式。

研究结果

本论文以慕尼黑城市为样本, 截取2015年13,545张图片。POIs由DBSCAN和k-means聚类分析将其分配到有名的POIs。由此, 本论文证明了两种技术对不同用法的各自优势。关联规则挖掘分析显示了显著联系(support:1%−4.6%;lift:1.4%−32.1%), 序列样式勘探分析确立了相关频率游览次序(support:0.6%−1.7%。

研究理论限制/意义

本论文的理论贡献在于, 根据图片数据, 通过对比分析不同聚类分析技术对确立POIs, 并且证明关联规则挖掘分析和序列样式勘探分析各有千秋又互相补充的分析技术以确立游客空间行为。

研究现实意义

本论文的现实意义在于, 强调了大数据的来源, 比如Flickr,证明了其对于有效代替传统数据的潜力, 以分析在游客在一个旅游目的地的空间行为和动线模式。特别是这种方法实现了实时自动可操作性等优势。

研究原创性/价值

本论文展示了一种方法, 这种方法通过聚类分析社交媒体上的上传图片以确立POIs, 以及通过关联规则挖掘分析和序列样式勘探分析来分析游客空间行为。本论文对于不同聚类分析以确立不同适用情况下的POIs的确立提出了独到见解。

Article
Publication date: 19 March 2021

Wai Tung Ho and Fu Wing Yu

This study aims to apply association rule mining (ARM) to uncover specific associations between operating components of a chiller system and improve its coefficient of…

189

Abstract

Purpose

This study aims to apply association rule mining (ARM) to uncover specific associations between operating components of a chiller system and improve its coefficient of performance (COP), hence reducing the electricity use of buildings with central air conditioning.

Design/methodology/approach

First, 13 operating variables were identified, comprising measures of temperatures and flow rates of system components and their switching statuses. The variables were grouped into four bins before carrying out ARM. Strong rules were produced to associate the variables and switching statuses with different COP classes.

Findings

The strong rules explain existing constraints on practising chiller sequencing and prioritise variables for optimisation. Based on strong rules for the highest COP class, the optimal operating strategy involves rescheduling chillers and their associated components in pairs during a high load operation. Resetting the chilled water supply temperature is the next best strategy, followed by resetting the condenser water entering temperature, subject to operating constraints.

Research limitations/implications

This study considers the even frequency method with four bins only. Replication work can be done with other discretisation methods and different numbers of classes to compare potential differences in the bin ranges of the optimised variables.

Practical implications

The strong rules identified by ARM highlight associations between variables and high or low COPs. This supports the selection of critical variables and the operating status of system components to maximise the COP. Tailor-made optimisation strategies and the associated electricity savings can be further evaluated.

Originality/value

Previous studies applied ARM for chiller fault detection but without considering system performance under the interaction of different components. The novelty of this study is its demonstration of ARM’s intelligence at discovering associations in past operating data. This enables the identification of tailor-made energy management opportunities, which are essential for all engineering systems. ARM is free from the prediction errors of typical regression and black-box models.

Open Access
Article
Publication date: 26 November 2018

Zhishuo Liu, Qianhui Shen, Jingmiao Ma and Ziqi Dong

This paper aims to extract the comment targets in Chinese online shopping platform.

Abstract

Purpose

This paper aims to extract the comment targets in Chinese online shopping platform.

Design/methodology/approach

The authors first collect the comment texts, word segmentation, part-of-speech (POS) tagging and extracted feature words twice. Then they cluster the evaluation sentence and find the association rules between the evaluation words and the evaluation object. At the same time, they establish the association rule table. Finally, the authors can mine the evaluation object of comment sentence according to the evaluation word and the association rule table. At last, they obtain comment data from Taobao and demonstrate that the method proposed in this paper is effective by experiment.

Findings

The extracting comment target method the authors proposed in this paper is effective.

Research limitations/implications

First, the study object of extracting implicit features is review clauses, and not considering the context information, which may affect the accuracy of the feature excavation to a certain degree. Second, when extracting feature words, the low-frequency feature words are not considered, but some low-frequency feature words also contain effective information.

Practical implications

Because of the mass online reviews data, reading every comment one by one is impossible. Therefore, it is important that research on handling product comments and present useful or interest comments for clients.

Originality/value

The extracting comment target method the authors proposed in this paper is effective.

Details

International Journal of Crowd Science, vol. 2 no. 3
Type: Research Article
ISSN: 2398-7294

Keywords

Open Access
Article
Publication date: 25 March 2020

Ana Odorović and Karsten Wenzlaff

The paper discusses the rationale for a widespread reliance on Codes of Conduct (CoC) in European crowdfunding through the lenses of economic theories of self-regulation…

1924

Abstract

Purpose

The paper discusses the rationale for a widespread reliance on Codes of Conduct (CoC) in European crowdfunding through the lenses of economic theories of self-regulation. By analysing the institutional design of CoCs in crowdfunding, the paper illustrates the differences in their regulatory context, inclusiveness, monitoring and enforcement. It offers the first systematic overview of substantial rules of CoCs in crowdfunding.

Design/methodology/approach

A comparative case study of nine CoCs in Europe is used to illustrate differences in their institutional design and discern the economic purpose of the CoC.

Findings

The institutional design of different CoCs in Europe mainly supports voluntary theories of self-regulation. In particular, the theory of reputation commons has the most explanatory power. The substantial rules of CoC in different markets show the potential sources of market failure through the perspectives of platforms.

Research limitations/implications

CoCs appear in various regulatory, cultural, and industry contexts of different countries. Some of the institutional design features of CoC might be a result of these characteristics.

Practical implications

Crowdfunding associations wishing to develop their own CoC may learn from a comparative overview of key provisions.

Social implications

For governments in Europe, contemplating creating or revising bespoke crowdfunding regimes, the paper identifies areas where crowdfunding platforms perceive market failure.

Originality/value

This paper is the first systematic study of self-regulatory institutions in European crowdfunding. The paper employs a theoretical framework for the analysis of self-regulation in crowdfunding and provides a comparison of a regulatory context, inclusiveness, monitoring and enforcement of different CoCs in Europe.

Details

Baltic Journal of Management, vol. 15 no. 2
Type: Research Article
ISSN: 1746-5265

Keywords

Article
Publication date: 24 June 2022

Zhao-ge Liu, Xiang-yang Li and Li-min Qiao

Process mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community…

Abstract

Purpose

Process mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community service domains with small datasets, the effects of process mining are generally limited due to process incompleteness and data noise. In this paper, a cross-domain knowledge transfer method is proposed to help service process discovery with small datasets by making use of rich knowledge in similar domains with large datasets.

Design/methodology/approach

First, ontology modeling is used to reduce the effects of cross-domain semantic ambiguity on knowledge transfer. Second, association rules (of the activities in the service processes) are extracted with Bayesian network. Third, applicable association rules are retrieved using an applicability assignment function. Further, the retrieved association rules in domains with large datasets are mapped to those with a small dataset using a linear programming method, with a heuristic miner being adopted to generate the process model.

Findings

The proposed method is verified based on the empirical data of 10 service domains from Beidaihe, China. Results show that process discovery performance of all 10 domains were improved with the overall robustness score, precision, recall and F1 score increased by 13%, 13%, 17% and 15%, respectively. For the domains with only small datasets, the cross-domain knowledge transfer method outperforms popular state-of-the art methods.

Originality/value

The limitations of sample sizes are greatly reduced. This scheme can be followed to establish business process management systems of community services with reasonable performance and limited sample sizes.

Details

Business Process Management Journal, vol. 28 no. 4
Type: Research Article
ISSN: 1463-7154

Keywords

Article
Publication date: 28 October 2021

Husna Sarirah Husin, James Thom and Xiuzhen Zhang

The purpose of the study is to use web serer logs in analyzing the changes of user behavior in reading online news, in terms of desktop and mobile users. Advances in…

109

Abstract

Purpose

The purpose of the study is to use web serer logs in analyzing the changes of user behavior in reading online news, in terms of desktop and mobile users. Advances in mobile technology and social media have paved the way for online news consumption to evolve. There is an absence of research into the changes of user behavior in terms of desktop versus mobile users, particularly by analyzing the server logs.

Design/methodology/approach

In this paper, the authors investigate the evolution of user behavior using logs from the Malaysian newspaper Berita Harian Online in April 2012 and April 2017. Web usage mining techniques were used for pre-processing the logs and identifying user sessions. A Markov model is used to analyze navigation flows, and association rule mining is used to analyze user behavior within sessions.

Findings

It was found that page accesses have increased tremendously, particularly from Android phones, and about half of the requests in 2017 are referred from Facebook. Navigation flow between the main page, articles and section pages has changed from 2012 to 2017; while most users started navigation with the main page in 2012, readers often started with an article in 2017. Based on association rules, National and Sports are the most frequent section pages in 2012 and 2017 for desktop and mobile. However, based on the lift and conviction, these two sections are not read together in the same session as frequently as might be expected. Other less popular items have higher probability of being read together in a session.

Research limitations/implications

The localized data set is from Berita Harian Online; although unique to this particular newspaper, the findings and the methodology for investigating user behavior can be applied to other online news. On another note, the data set could be extended to be more than a month. Although initially data for the year 2012 was collected, unfortunately only the data for April 2012 is complete. Other months have missing days. Therefore, to make an impartial comparison for the evolution of user behavior in five years, the Web server logs for April 2017 were used.

Originality/value

The user behavior in 2012 and 2017 was compared using association rules and Markov flow. Different from existing studies analyzing online newspaper Web server logs, this paper uniquely investigates changes in user behavior as a result of mobile phones becoming a mainstream technology for accessing the Web.

Details

International Journal of Web Information Systems, vol. 18 no. 1
Type: Research Article
ISSN: 1744-0084

Keywords

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