scholarly journals A Model for Trend Analysis in the Online Shopping Scenario Using Multilevel Hesitation Pattern Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Abhishek Dixit ◽  
Akhilesh Tiwari ◽  
R. K. Gupta
Keyword(s):  
Pattern Mining ◽  
Online Shopping ◽  
Decision Makers ◽  
Rule Mining ◽  
Usual Practice ◽  
Confidence Threshold ◽  
Proposed Model ◽  
Support Threshold ◽  
Multiple Levels ◽  
Conceptual Hierarchy

The present paper proposes a new model for the exploration of hesitated patterns from multiple levels of conceptual hierarchy in the transactional dataset. The usual practice of mining patterns has focused on identifying frequent patterns (i.e., which occur together) in the transactional dataset but uncovers the vital information about the patterns which are almost frequent (but not exactly frequent) called “hesitated patterns.” The proposed model uses the reduced minimum support threshold (contains two values: attractiveness and hesitation) and constant minimum confidence threshold with the top-down progressive deepening approach for generating patterns and utilizing the apriori property. To validate the model, an online purchasing scenario of books through e-commerce-based online shopping platforms such as Amazon has been considered and shown that how the various factors contributed towards building hesitation to purchase a book at the time of purchasing. The present work suggests a novel way for deriving hesitated patterns from multiple levels in the conceptual hierarchy with respect to the target dataset. Moreover, it is observed that the concepts and theories available in the existing related work Lu and Ng (2007) are only focusing on the introductory aspect of vague set theory-based hesitation association rule mining, which is not useful for handling the patterns from multiple levels of granularity, while the proposed model is complete in nature and addresses the very significant and untouched problem of mining “multilevel hesitated patterns” and is certainly useful for exploring the hesitated patterns from multiple levels of granularity based on the considered hesitation status in a transactional dataset. These hesitated patterns can be further utilized by decision makers and business analysts to build the strategy on how to increase the attraction level of such hesitated items (appeared in a particular transaction/set of transactions in a given dataset) to convert their state from hesitated to preferred items.

scholarly journals Sistem Rekomendasi Penjualan Menu Makanan di UMKM Kuliner Menggunakan Association Rule

2021 ◽  
Vol 19 (2) ◽  
pp. 87-90
Author(s):  
Ade Kania Ningsih ◽  
Wina Witanti
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Large Scale ◽  
Apriori Algorithm ◽  
Process Data ◽  
Rule Mining ◽  
Confidence Threshold ◽  
Support Threshold ◽  
On Line

Micro, Small and Medium Enterprises (MSMEs) are one of the driving motors of the economy in the country, even MSMEs are the backbone of the Economy in Indonesia. MSMEs in Indonesia account for about 60% of GDP (Gross Domestic Product) and also provide employment opportunities to the community. However, with the emergence of THE COVID-19 outbreak of MSMEs in West Java there has been a decrease of up to 80%. This is a problem that exists, MSMEs customers are segmented based on the region due to large-scale social restrictions. This research conducted a review of product sales recommendation system in on-line shop using association rule mining in the culinary industry sector. The research begins with data selection, pre-process data, and data transformation, then the data that has been cleaned will be tested with A priori algorithm. The rules will evaluate using support, confidence, and an upgrade value to determine whether it's the best rule or not. The results of this study are software that will calculate the formation of association rules between culinary products. After an experiment with data amounting to 100 data, an association rule was obtained in the form of a certain pattern of customer behavior, by using Association Rules Technique and Apriori Algorithm, 12 rules are generated with a support threshold of 5% and a confidence threshold of 80%.  , Usaha Kecil dan Menengah (UMKM) merupakan salah satu motor penggerak perekonomian dalam negeri, bahkan UMKM merupakan tulang punggung Perekonomian di Indonesia. UMKM di Indonesia menyumbang sekitar 60% dari PDB (Produk Domestik Bruto) dan juga memberikan kesempatan kerja kepada masyarakat. Namun dengan munculnya Wabah COVID-19 pada UMKM di Jawa Barat terjadi penurunan hingga 80%. Hal ini menjadi permasalahan yang ada, nasabah UMKM tersegmentasi berdasarkan wilayah karena adanya pembatasan sosial berskala besar. Penelitian ini melakukan review terhadap sistem rekomendasi penjualan produk di toko on-line dengan menggunakan Association rule mining pada sektor industri kuliner. Penelitian diawali dengan pemilihan data, data praproses, dan transformasi data, kemudian data yang telah dibersihkan akan diuji dengan algoritma apriori. Aturan akan mengevaluasi menggunakan dukungan, keyakinan, dan nilai peningkatan untuk menentukan apakah itu aturan terbaik atau bukan. Hasil dari penelitian ini berupa software yang akan menghitung pembentukan aturan asosiasi antar produk kuliner. Setelah dilakukan percobaan dengan data sebanyak 100 data, diperoleh aturan asosiasi berupa pola perilaku konsumen tertentu, dengan menggunakan Association Rules Technique dan Apriori Algorithm dihasilkan 12 aturan dengan support threshold 5% dan confidence threshold. dari 80%. 

Two-level multi-criteria comprehensive evaluation for preference vectors in online shopping platform evaluation

2020 ◽  
Vol 39 (5) ◽  
pp. 7921-7930
Author(s):  
Liu Fan ◽  
Ronald R. Yager ◽  
Radko Mesiar ◽  
LeSheng Jin
Keyword(s):  
Online Shopping ◽  
Comprehensive Evaluation ◽  
Evaluation Model ◽  
Decision Makers ◽  
Main Indicator ◽  
Proposed Model ◽  
Detailed Evaluation ◽  
Negative Effect ◽  
High Consistency ◽  
Evaluation Information

The evaluation for online shopping platform is the basis for further decision and policy taking. The collected individual opinion and evaluation information are often represented by some linguistic/preference vectors. Further aggregating those vector needs to simultaneously consider two contradictory factors: the original weights assigned and the inconsistencies involved which requires some new weights assigned. Around those weights allocation factors, to mitigate the negative effect of inconsistency in the collected information, we propose an integrated evaluation model. The model uses the scatter degree as a main indicator, and extends some weights allocation methods such as regular increasing monotone (RIM) quantifier based weights allocation in a new environment, and applies the three sets expression based paradigm and formulation. The proposed model is able to simultaneously give emphasis on those input data with high consistency and to consider the preferences of decision makers. Some detailed evaluation processes and numerical examples are also provided for practitioners to refer to.

Decision making based on intuitionistic fuzzy preference relations with additive approximate consistency

2020 ◽  
Vol 39 (3) ◽  
pp. 4041-4058
Author(s):  
Fang Liu ◽  
Xu Tan ◽  
Hui Yang ◽  
Hui Zhao
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Real Interval ◽  
Preference Relations ◽  
Intuitionistic Fuzzy ◽  
Proposed Model ◽  
Fuzzy Preference Relations ◽  
Novel Concept ◽  
Fuzzy Weights ◽  
Fuzzy Preference

Intuitionistic fuzzy preference relations (IFPRs) have the natural ability to reflect the positive, the negative and the non-determinative judgements of decision makers. A decision making model is proposed by considering the inherent property of IFPRs in this study, where the main novelty comes with the introduction of the concept of additive approximate consistency. First, the consistency definitions of IFPRs are reviewed and the underlying ideas are analyzed. Second, by considering the allocation of the non-determinacy degree of decision makers’ opinions, the novel concept of approximate consistency for IFPRs is proposed. Then the additive approximate consistency of IFPRs is defined and the properties are studied. Third, the priorities of alternatives are derived from IFPRs with additive approximate consistency by considering the effects of the permutations of alternatives and the allocation of the non-determinacy degree. The rankings of alternatives based on real, interval and intuitionistic fuzzy weights are investigated, respectively. Finally, some comparisons are reported by carrying out numerical examples to show the novelty and advantage of the proposed model. It is found that the proposed model can offer various decision schemes due to the allocation of the non-determinacy degree of IFPRs.

scholarly journals Optimizing a Reverse Supply Chain Network for Electronic Waste under Risk and Uncertain Factors

2021 ◽  
Vol 11 (4) ◽  
pp. 1946
Author(s):  
Linh Thi Truc Doan ◽  
Yousef Amer ◽  
Sang-Heon Lee ◽  
Phan Nguyen Ky Phuc ◽  
Tham Thi Tran
Keyword(s):  
Supply Chain ◽  
Electronic Waste ◽  
Decision Makers ◽  
Reverse Supply Chain ◽  
Satisfactory Solution ◽  
Level Of Satisfaction ◽  
Proposed Model ◽  
Objective Value ◽  
The Right ◽  
The Impact

Minimizing the impact of electronic waste (e-waste) on the environment through designing an effective reverse supply chain (RSC) is attracting the attention of both industry and academia. To obtain this goal, this study strives to develop an e-waste RSC model where the input parameters are fuzzy and risk factors are considered. The problem is then solved through crisp transformation and decision-makers are given the right to choose solutions based on their satisfaction. The result shows that the proposed model provides a practical and satisfactory solution to compromise between the level of satisfaction of constraints and the objective value. This solution includes strategic and operational decisions such as the optimal locations of facilities (i.e., disassembly, repairing, recycling facilities) and the flow quantities in the RSC.

scholarly journals A Model for Analysing the Collective Dynamic Behaviour and Characterising the Exploitation of Population-Based Algorithms

2014 ◽  
Vol 22 (1) ◽  
pp. 159-188 ◽  
Author(s):  
Mikdam Turkey ◽  
Riccardo Poli
Keyword(s):  
Population Dynamics ◽  
Dynamic Behaviour ◽  
Fitness Landscape ◽  
Population Based ◽  
Levels Of Abstraction ◽  
Collective Dynamic ◽  
Model Studies ◽  
Proposed Model ◽  
Wide Range ◽  
Multiple Levels

Several previous studies have focused on modelling and analysing the collective dynamic behaviour of population-based algorithms. However, an empirical approach for identifying and characterising such a behaviour is surprisingly lacking. In this paper, we present a new model to capture this collective behaviour, and to extract and quantify features associated with it. The proposed model studies the topological distribution of an algorithm's activity from both a genotypic and a phenotypic perspective, and represents population dynamics using multiple levels of abstraction. The model can have different instantiations. Here it has been implemented using a modified version of self-organising maps. These are used to represent and track the population motion in the fitness landscape as the algorithm operates on solving a problem. Based on this model, we developed a set of features that characterise the population's collective dynamic behaviour. By analysing them and revealing their dependency on fitness distributions, we were then able to define an indicator of the exploitation behaviour of an algorithm. This is an entropy-based measure that assesses the dependency on fitness distributions of different features of population dynamics. To test the proposed measures, evolutionary algorithms with different crossover operators, selection pressure levels and population handling techniques have been examined, which lead populations to exhibit a wide range of exploitation-exploration behaviours.

An Efficient Method for Mining Rare Association Rules: A Case Study on Air Pollution

2021 ◽  
Vol 30 (04) ◽  
pp. 2150018
Author(s):  
Anindita Borah ◽  
Bhabesh Nath
Keyword(s):  
Air Pollution ◽  
Association Rules ◽  
Air Pollutants ◽  
Pattern Mining ◽  
Environmental Parameters ◽  
Pollution Monitoring ◽  
Air Pollution Monitoring ◽  
Rare Association ◽  
Support Threshold ◽  
Complete Set

Most pattern mining techniques almost singularly focus on identifying frequent patterns and very less attention has been paid to the generation of rare patterns. However, in several domains, recognizing less frequent but strongly related patterns have greater advantage over the former ones. Identification of compelling and meaningful rare associations among such patterns may proved to be significant for air quality management that has become an indispensable task in today’s world. The rare correlations between air pollutants and other parameters may aid in restricting the air pollution to a manageable level. To this end, efficient and competent rare pattern mining techniques are needed that can generate the complete set of rare patterns, further identifying significant rare association rules among them. Moreover, a notable issue with databases is their continuous update over time due to the addition of new records. The users requirement or behavior may change with the incremental update of databases that makes it difficult to determine a suitable support threshold for the extraction of interesting rare association rules. This paper, presents an efficient rare pattern mining technique to capture the complete set of rare patterns from a real environmental dataset. The proposed approach does not restart the entire mining process upon threshold update and generates the complete set of rare association rules in a single database scan. It can effectively perform incremental mining and also provides flexibility to the user to regulate the value of support threshold for generating the rare patterns. Significant rare association rules representing correlations between air pollutants and other environmental parameters are further extracted from the generated rare patterns to identify the substantial causes of air pollution. Performance analysis shows that the proposed method is more efficient than existing rare pattern mining approaches in providing significant directions to the domain experts for air pollution monitoring.

scholarly journals Interval Forecasting in Supply Chain with Small Sample

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Lifeng Wu ◽  
Yan Chen
Keyword(s):  
Supply Chain ◽  
Small Sample ◽  
Decision Makers ◽  
Small Samples ◽  
Forecasting Model ◽  
Future Trends ◽  
Human Judgment ◽  
Proposed Model ◽  
Grey Models ◽  
Short Time

To deal with the forecasting with small samples in the supply chain, three grey models with fractional order accumulation are presented. Human judgment of future trends is incorporated into the order number of accumulation. The output of the proposed model will provide decision-makers in the supply chain with more forecasting information for short time periods. The results of practical real examples demonstrate that the model provides remarkable prediction performances compared with the traditional forecasting model.

scholarly journals A Cellular Automata Model of the Relationship between Adverse Events and Regional Infrastructure Development in an Active War Theater

Technologies ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 54
Author(s):  
Bozkurt ◽  
Karwowski ◽  
Çakıt ◽  
Ahram
Keyword(s):  
Adverse Event ◽  
Cellular Automata ◽  
Adverse Events ◽  
Decision Makers ◽  
Development Projects ◽  
Infrastructure Development ◽  
Complex Interactions ◽  
Proposed Model ◽  
Ca Model ◽  
Complex Formations

This study presents a cellular automata (CA) model to assist decision-makers in understanding the effects of infrastructure development projects on adverse events in an active war theater. The adverse events are caused by terrorist activities that primarily target the civilian population in countries such as Afghanistan. In the CA-based model, cells in the same neighborhood synchronously interact with one another to determine their next states, and small changes in iteration yield to complex formations of adverse event risks. The results demonstrate that the proposed model can help in the evaluation of infrastructure development projects in relation to changes in the reported adverse events, as well as in the identification of the geographical locations, times, and impacts of such developments. The results also show that infrastructure development projects have different impacts on the reported adverse events. The CA modeling approach can be used to support decision-makers in allocating infrastructure development funds to stabilize active war regions with higher adverse event risks. Such models can also improve the understanding of the complex interactions between infrastructure development projects and adverse events.

scholarly journals Interbasin water transfer in a changing world: A new conceptual model

2021 ◽  
pp. 030913332110650
Author(s):  
Edward Rollason ◽  
Pammi Sinha ◽  
Louise J Bracken
Keyword(s):  
Water Scarcity ◽  
Conceptual Models ◽  
Management Strategies ◽  
Temporal Distribution ◽  
Water Transfer ◽  
Decision Makers ◽  
Global Issue ◽  
Proposed Model ◽  
Interbasin Water Transfer ◽  
Water Scarce

Water scarcity is a global issue, affecting in excess of four billion people. Interbasin Water Transfer (IBWT) is an established method for increasing water supply by transferring excess water from one catchment to another, water-scarce catchment. The implementation of IBWT peaked in the 1980s and was accompanied by a robust academic debate of its impacts. A recent resurgence in the popularity of IBWT, and particularly the promotion of mega-scale schemes, warrants revisiting this technology. This paper provides an updated review, building on previously published work, but also incorporates learning from schemes developed since the 1980s. We examine the spatial and temporal distribution of schemes and their drivers, review the arguments for and against the implementation of IBWT schemes and examine conceptual models for assessing IBWT schemes. Our analysis suggests that IBWT is growing in popularity as a supply-side solution for water scarcity and is likely to represent a key tool for water managers into the future. However, we argue that IBWT cannot continue to be delivered through current approaches, which prioritise water-centric policies and practices at the expense of social and environmental concerns. We critically examine the Socio-Ecological Systems and Water-Energy-Food (WEF) Nexus models as new conceptual models for conceptualising and assessing IBWT. We conclude that neither model offers a comprehensive solution. Instead, we propose an enhanced WEF model (eWEF) to facilitate a more holistic assessment of how these mega-scale engineering interventions are integrated into water management strategies. The proposed model will help water managers, decision-makers, IBWT funders and communities create more sustainable IBWT schemes.

scholarly journals A REVIEW ON TECHNOLOGY READINESS CONCEPT TO EXPLAIN CONSUMER’S ONLINE PURCHASE INTENTION

2020 ◽  
Vol 6 ◽  
pp. 49-57
Author(s):  
Khairull Anuar Ismail ◽  
Nabsiah Abdul Wahid
Keyword(s):  
Purchase Intention ◽  
Online Shopping ◽  
New Technology ◽  
Technology Readiness ◽  
Online Purchase ◽  
Intention To Purchase ◽  
Proposed Model ◽  
Related Service ◽  
Adoption Of New Technology ◽  
The Relationship

Recently in Malaysia, a substantial number of consumers have been found to be avoiding online shopping as they prefer to shop in physical stores. This scenario brings up the issue of whether Malaysian consumers are ready technologically to shop online. To tackle this issue, a review of the concept of technology readiness is made to help explain Malaysian consumers’ online purchase intention behaviour. Technology readiness is chosen here because the concept reflects an individual’s predisposition in the usage and adoption of new technology. For the purpose of this review, this study selects technology readiness concept as proposed by Parasuraman  (2000). From the review, this study found that technology readiness has been measured in the past either as a single (unidimensional) or a multidimensional construct involving four factors, namely, optimism, innovativeness, discomfort and insecurity. A summary on past researchers’ findings in identifying the relationship between technology readiness (and its proposed dimensions) with technology usage is included in this review. For example, technology readiness was found to have a significant influence on behavioural intention in using mobile commerce to purchase travel-related service. Additionally, technology readiness motivator (optimism and innovativeness) and inhibitor (discomfort and insecurity) were identified to be related to intention to use technology. Based on the review, this study proposes a model to help explain the user’s intention to purchase online situation. In the proposed model, both technology readiness motivators and inhibitors are suggested to show positive and negative influences respectively on the user’s intention to purchase online. This review is thought to be beneficial to many. For instance, researchers would find insights on the usefulness of technology readiness and on how it has been and can be applied for further investigation. As for marketing practitioners, the review would help guide them understand the influence technology readiness has on consumers behaviour intention in adopting online shopping which they could apply for future marketing strategy.

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