Thoughts on Women Entrepreneurship: An Application of Market Basket Analysis with Google Trends Data

2021 ◽  
Author(s):  
Anıl Boz Semerci ◽  
Ayşe Abbasoğlu Özgören ◽  
Duygu İçen
Keyword(s):  
Time Series ◽  
Social Justice ◽  
Geographic Area ◽  
Google Trends ◽  
Digital Data ◽  
Specific Topic ◽  
Market Basket Analysis ◽  
Women Entrepreneurship ◽  
Market Basket ◽  
Regional Setting

Abstract This paper focuses on the popularity and awareness of keywords in Google Trends data related to entrepreneurship of women in a global and cross-regional setting by using market basket analysis. Google Trends is one of the digital data platforms that provides a time series index of the volume of queries users enter into Google in a given geographic area. It is the most popular tool for gathering any information, and it has been used in several topics. Market basket analysis indicates items that appear/used together and the frequency of these appearances. Such technique is appropriate in finding hidden associations between items, which is also crucial in assessing individuals’ thoughts on a specific topic. This study contributes to the literature in terms of being the first study to use market basket analysis on Google trends data in the context of women’s entrepreneurship finding hidden associations between items, which is crucial in assessing individuals’ thoughts on a specific topic. The results of the analysis are interpreted through the lens of genderresponsive strategies, equality, efficiency and social justice in different country and region contexts.

Market basket applications on short web links

2019 ◽  
Vol 62 (2) ◽  
pp. 139-157
Author(s):  
James Gallagher ◽  
Christopher M Smith
Keyword(s):  
Graph Mining ◽  
Geographic Area ◽  
Space Environment ◽  
Time Range ◽  
Web Traffic ◽  
Market Basket Analysis ◽  
Market Basket ◽  
The Past ◽  
Data Collection And Analysis ◽  
Hardware Platforms

Market research is an indispensable part of an organization’s ability to understand market dynamics. Over the past 20 years, data collection and analysis through Knowledge Discovery through Databases (KDD) has arisen to supplement the traditional methods of surveys and focus groups. Market Basket Analysis is a discipline of KDD that identifies associations between commonly purchased items. As social media use has grown, link shortening companies help users share links in a constrained space environment and, in exchange, collect data about each user when a link is clicked. This research applies market basket analysis techniques with graph mining to shortened web link data to identify communities of co-visited websites to help analysts better understand web traffic for a geographic area during a time range. Patterns within clusters of web domains regarding hardware platforms, operating systems, or referral sources are then identified and used to gain a better understanding of a geographic area.

MODEL SISTEM INFORMASI PEMASARAN SEBAGAI PENUNJANG KEPUTUSAN PROMOSI PRODUK MENGGUNAKAN ALGORITMA MAX -MINER

ICIT Journal ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 94-104
Author(s):  
Fernando Siboro ◽  
Capri Eriansyah ◽  
Muhammad Adi Sofyan
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Market Basket Analysis ◽  
Market Basket

Teknologi informasi saat ini terus berkembang semakin cepat, membuat pola berfikir manusia berubah, dengan proses pertumbuhan yang seperti ini, generasi akan datang diharuskan mempunyai keahlian yang lebih baik di bidang pemanfaatan teknologi informasi. Kebutuhan adanya kemudahan dari segi pemasaran, saat ini dirasa sangat penting, terutama bagi perusahaan yang bergerak dibidang penjulan atau distributor guna menunjang meningkatkan akurasi dan kualitas pemasaran itu sendiri. Namun pada kenyataanya, sistem yang berjalan masih tergolong kurang efektif dan efesien dalam melayani kebutuhan pelanggan, hal ini dikarenakan sistem pemasaran produk hanya bisa diakses secara manual, dan belum adanya media informasi seputar produk yang ditawarkan, oleh sebab itu dibuatlah suatu perancangan sistem informasi yang mengatur pemasaran produk dan dapat menjadi bahan dalam pembuatan laporan sistem penunjang keputusan. Dalam perancangan ini menggunakan metode data mining market basket analysis dan Max-Miner sebagai algoritma. Serta menggunakan metode penerapan sistem waterfall atau sering dinamakan siklus hidup klasik (classic life cycle). Dengan demikian rancang bangun sistem informasi ini, mengacu kepada bagaimana cara agar pemasaran produk dapat di akses dengan mudah, cepat, dan akurat dimanapun dan kapanpun, calon customer dapat mengakses tanpa terkendala waktu dan tempat, serta menjadi wadah dalam pengambilan keputusan oleh perusahaan. Metodologi desain menggunakan uml yang melimuti usecase, activity, squence dan untuk pengelolaan basis data menggunakan mysql. Sistem ini diharapkan mampu dijadikan salah satu penunjang keputusan untuk kebutuhan promosi produk. Kata Kunci: Penunjang pemasaran, promosi produk, algoritma Max-Miner

scholarly journals Application of uninorms to market basket analysis

2018 ◽  
Vol 34 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Raymond Moodley ◽  
Francisco Chiclana ◽  
Fabio Caraffini ◽  
Jenny Carter
Keyword(s):  
Market Basket Analysis ◽  
Market Basket

scholarly journals Fractional differencing in stock market price and online presence of global tourist corporations

2019 ◽  
Vol 24 (48) ◽  
pp. 194-204 ◽  
Author(s):  
Francisco Flores-Muñoz ◽  
Alberto Javier Báez-García ◽  
Josué Gutiérrez-Barroso
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Market Price ◽  
Google Trends ◽  
Data Sets ◽  
Market Prices ◽  
Content Type ◽  
Search Results ◽  
Online Presence ◽  
Fractional Differencing

Purpose This work aims to explore the behavior of stock market prices according to the autoregressive fractional differencing integrated moving average model. This behavior will be compared with a measure of online presence, search engine results as measured by Google Trends. Design/methodology/approach The study sample is comprised by the companies listed at the STOXX® Global 3000 Travel and Leisure. Google Finance and Yahoo Finance, along with Google Trends, were used, respectively, to obtain the data of stock prices and search results, for a period of five years (October 2012 to October 2017). To guarantee certain comparability between the two data sets, weekly observations were collected, with a total figure of 118 firms, two time series each (price and search results), around 61,000 observations. Findings Relationships between the two data sets are explored, with theoretical implications for the fields of economics, finance and management. Tourist corporations were analyzed owing to their growing economic impact. The estimations are initially consistent with long memory; so, they suggest that both stock market prices and online search trends deserve further exploration for modeling and forecasting. Significant differences owing to country and sector effects are also shown. Originality/value This research contributes in two different ways: it demonstrate the potential of a new tool for the analysis of relevant time series to monitor the behavior of firms and markets, and it suggests several theoretical pathways for further research in the specific topics of asymmetry of information and corporate transparency, proposing pertinent bridges between the two fields.

A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

2011 ◽  
Vol 145 ◽  
pp. 292-296
Author(s):  
Lee Wen Huang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Execution Time ◽  
Hybrid Algorithm ◽  
Hybrid Approach ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Large Databases ◽  
Closed Itemsets ◽  
Simulation Results

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

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