scholarly journals Data mining in the context of urban metabolism: A case study of Geneva and Lausanne, Switzerland

2021 ◽  
Vol 2042 (1) ◽  
pp. 012020
Author(s):  
N S Wiedmann ◽  
A Athanassiadis ◽  
C R Binder
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Large Data ◽  
Mitigation Strategies ◽  
Urban Metabolism ◽  
Multiple Sources ◽  
Data Accessibility ◽  
Current Configuration ◽  
Use Of Data

Abstract The highest share of the global population lives in cities. The current configuration of the latter requires considerable amounts of resource flows causing the degradation of local and global ecosystems. To face the complexity of these challenges, scientists use the concept of urban metabolism (UM), i.e. measuring urban input and output flows from a systemic perspective. This accounting method results in a large data collection from multiple sources that are often not harmonised. Metabolism of Cities Data Hub is an online platform which facilitates data collection, processing and visualisation in order to extract urban metabolism insights. This work highlights the challenges faced when mining urban metabolism data in the case of Lausanne and Geneva, as well as provides insights on how data could be best used from users and providers. Slight differences between the two case studies, in terms of data accessibility and availability where experienced but the main challenges revolved around data copyright, format and availability. As a conclusion, the used tool can enable harmonisation and standardisation of UM data. As such it could contribute to the use of data mining to streamline the environmental monitoring of cities as well as facilitate the creation of mitigation strategies.

scholarly journals PENERAPAN DATA MINING MENGGUNAKAN ALGORITMA C4.5 TEHADAP PENGARUH PENJUALAN KOPI PADA PT. JPW INDONESIA

2020 ◽  
Vol 3 (1) ◽  
pp. 40-54
Author(s):  
Ikong Ifongki
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Rules ◽  
Large Data ◽  
Added Value ◽  
Data Set ◽  
Use Of Data ◽  
Decision Tree Classification ◽  
C4.5 Algorithm

Data mining is a series of processes to explore the added value of a data set in the form of knowledge that has not been known manually. The use of data mining techniques is expected to provide knowledge - knowledge that was previously hidden in the data warehouse, so that it becomes valuable information. C4.5 algorithm is a decision tree classification algorithm that is widely used because it has the main advantages of other algorithms. The advantages of the C4.5 algorithm can produce decision trees that are easily interpreted, have an acceptable level of accuracy, are efficient in handling discrete type attributes and can handle discrete and numeric type attributes. The output of the C4.5 algorithm is a decision tree like other classification techniques, a decision tree is a structure that can be used to divide a large data set into smaller sets of records by applying a series of decision rules, with each series of division members of the resulting set become similar to each other. In this case study what is discussed is the effect of coffee sales by processing 106 data from 1087 coffee sales data at PT. JPW Indonesia. Data samples taken will be calculated manually using Microsoft Excel and Rapidminer software. The results of the calculation of the C4.5 algorithm method show that the Quantity and Price attributes greatly affect coffee sales so that sales at PT. JPW Indonesia is still often unstable.

scholarly journals Análise do banco de atendimentos da Defesa Civil do Estado de São Paulo

2010 ◽  
Vol 33 (1) ◽  
pp. 35-43
Author(s):  
Diego José Chagas ◽  
Chou Sin Chan ◽  
Alessandra Cristina Corsi
Keyword(s):  
Data Mining ◽  
Sao Paulo ◽  
Data Organization ◽  
São Paulo ◽  
Data Mining Technique ◽  
Landslide Occurrence ◽  
Serra Do Mar ◽  
Use Of Data ◽  
Relevant Factors

In recent years the simple data organization is no longer a differential factor for institutions, since, depending on their volume, the traditional method of analysis and interpretation is extremely slow and costly. The use of data mining techniques is an alternative to allow this process semi-automatic. The objective of this work is to carry out a case study of data mining technique based on the WEKA software applied to hydrometeorological and geomorphological data which were collected in the Serra do Mar region of São Paulo State. Results obtained from the application of the association technique indicate that the presence of rock and boulders at terrains with scars and high declivity are relevant factors for the landslide occurrence.

scholarly journals KAJIAN PERILAKU BELANJA KONSUMEN MENGGUNAKAN TEKNIK ASOSIASI DI SUPERMARKET (STUDI KASUS: TOSERBA X). [STUDY OF CONSUMER SHOPPING BEHAVIOR USING ASSOCIATION TECHNIQUES IN SUPERMARKET (CASE STUDY: TOSERBA X)]

2020 ◽  
Vol 15 (2) ◽  
pp. 282
Author(s):  
Ronny Samsul Bahri ◽  
Laura Lahindah
Keyword(s):  
Data Mining ◽  
Shopping Behavior ◽  
Mining Method ◽  
Consumer Spending ◽  
Market Basket Analysis ◽  
Association Pattern ◽  
Use Of Data ◽  
Spending Patterns ◽  
Bahasa Indonesia

<p><em>The development of retail companies in Indonesia is quite rapid causing the need for the use of data as a basis for decision making. As one of the developing retail stores, the floor display pattern has not been well managed and has not been linked to the pattern of consumer spending. Market basket analysis is one of the data mining method techniques to determine the association of consumer spending patterns in a purchase transaction. This study aims to determine whether there is an association pattern in each term of consumer spending in five divisions of supermarket products (all divisions, food, non-food, household or GMS &amp; fresh). The term is divided into three, namely, term I (1-10), term II (11-20) and term III (21-month end). The data is processed using software Rapidminer version 5. The data processing results show an association relationship in several terms, namely all divisions in term I have influence, term II has no influence, term III has influence. Food division in term I has an influence, term II has no influence, term III has an effect. The nonfood division in term I has no influence, term II has no influence, term III has no effect. The GMS division in term I has no influence, term II has no influence, term III has no effect. The fresh division in term I has influence, term II has influence, term III has no effect. By using the results of the analysis, floor display and promotion patterns can be adjusted according to the consumer's shopping patterns.</em><strong> </strong></p><p><strong>Abstrak dalam Bahasa Indonesia.</strong>Perkembangan perusahaan ritel di Indonesia yang cukup pesat menyebabkan perlunya pemanfaatan data sebagai dasar dalam pengambilan keputusan.  Sebagai salah satu toko ritel yang sedang berkembang, pola pemajangan floor diplay belum dikelola dengan baik dan belum dikaitkan dengan pola belanja konsumennya.  M<em>arket basket analysi</em><em>s merupakan salah satu teknik metoda</em> <em>data mining</em> untuk menentukan hubungan asosiasi pola belanja kosumen dalam suatu transaksi pembelian.  Penelitian ini bertujuan untuk mengetahui apakah terdapat pola asosiasi pada setiap termin pembelanjaan konsumen pada lima divisi produk supermarket (seluruh divisi, food, nonfood, household atau GMS &amp; fresh). Termin terbagi menjadi tiga yaitu, termin I (tanggal 1-10), termin II (tanggal 11-20) dan termin III (tanggal 21-akhir bulan).  Data diolah dengan menggunakan Software Rapidminer versi 5. Hasil pengolahan data menunjukkan adanya hubungan asosiasi pada beberapa termin yaitu Seluruh divisi dalam termin I ada pengaruh, termin II tidak ada pengaruh, termin III ada pengaruh. Divisi food dalam termin I ada pengaruh, termin II tidak ada pengaruh, termin III ada pengaruh.  Divisi nonfood dalam termin I tidak ada pengaruh, termin II tidak ada pengaruh, termin III tidak ada pengaruh. Divisi GMS dalam termin I ada pengaruh, termin II tidak ada pengaruh, termin III tidak ada pengaruh. Divisi fresh dalam termin I ada pengaruh, termin II ada pengaruh, termin III tidak ada pengaruh. Dengan menggunakan hasil analisis, pola pemajangan floor display dan promosi dapat diselaraskan sesuai dengan pola belanja konsumen tersebut.</p>

A Hybrid Method for Prediction and Assessment Efficiency of Decision Making Units

2013 ◽  
Vol 5 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Iman Rahimi ◽  
Reza Behmanesh ◽  
Rosnah Mohd. Yusuff
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Rules ◽  
Large Data ◽  
Poultry Meat ◽  
Small Data ◽  
Data Set ◽  
Data Mining Techniques ◽  
Decision Making Units

The objective of this article is an evaluation and assessment efficiency of the poultry meat farm as a case study with the new method. As it is clear poultry farm industry is one of the most important sub- sectors in comparison to other ones. The purpose of this study is the prediction and assessment efficiency of poultry farms as decision making units (DMUs). Although, several methods have been proposed for solving this problem, the authors strongly need a methodology to discriminate performance powerfully. Their methodology is comprised of data envelopment analysis and some data mining techniques same as artificial neural network (ANN), decision tree (DT), and cluster analysis (CA). As a case study, data for the analysis were collected from 22 poultry companies in Iran. Moreover, due to a small data set and because of the fact that the authors must use large data set for applying data mining techniques, they employed k-fold cross validation method to validate the authors’ model. After assessing efficiency for each DMU and clustering them, followed by applied model and after presenting decision rules, results in precise and accurate optimizing technique.

scholarly journals Application Of Pizza Sales Data Mining Using Apriori Method

SinkrOn ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 1 ◽  
Author(s):  
Rusdiansyah Rusdiansyah ◽  
Nining Suharyanti ◽  
Triningsih Triningsih ◽  
Muhammad Darussalam
Keyword(s):  
Data Mining ◽  
Data Collection ◽  
Association Rules ◽  
A Priori ◽  
Apriori Algorithm ◽  
Mining Method ◽  
Processed Food ◽  
Sales Data ◽  
Business Opportunity ◽  
Use Of Data

Pizza is a processed food originating from Italy and has been spread in various other countries including one of them in Indonesia. Pizza is a processed food that is currently sought after by various groups of people so as to make the pizza business opportunity very profitable, if it is run in a food business. Currently the pizza business has very favorable prospects when compared to other businesses. Moreover, the targeted target can be from all walks of life from children to adults. Pizza sales transactions that produce sales data every day, have not been able to maximize the use of sales data. Sales data is only stored as an archive, so it becomes a pile of data. Therefore the use of data mining is used to solve this problem. A priori algorithm is a data mining method by using minimum support parameters, minimum confidence and will analyze in the period of every month of sales transactions. This study produces data on the results of the process of association rules from the data collection of sales transactions. From the association rules it can be concluded that the pattern of pizza sales, where consumers more often buy Meatzza and Cheese Mania, as evidenced by the results of calculations using Apriori Algorithm and Rapidminer 5.3, with support of 30% and 60% confidence.

scholarly journals Assessment of the Methodological Rigor of Case Studies in the Field of Management Accounting Published in Journals in Brazil

2015 ◽  
Vol 26 (67) ◽  
pp. 27-42 ◽  
Author(s):  
Kelly Cristina Mucio Marques ◽  
Reinaldo Rodrigues Camacho ◽  
Caio Cesar Violin de Alcantara
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Case Studies ◽  
Management Accounting ◽  
Research Strategy ◽  
Collection Method ◽  
Data Collection Method ◽  
Use Of Data ◽  
Methodological Rigor

This study aims to assess the methodological rigor of case studies in management accounting published in Brazilian journals. The study is descriptive. The data were collected using documentary research and content analysis, and 180 papers published from 2008 to 2012 in accounting journals rated as A2, B1, and B2 that were classified as case studies were selected. Based on the literature, we established a set of 15 criteria that we expected to be identified (either explicitly or implicitly) in the case studies to classify those case studies as appropriate from the standpoint of methodological rigor. These criteria were partially met by the papers analyzed. The aspects less aligned with those proposed in the literature were the following: little emphasis on justifying the need to understand phenomena in context; lack of explanation of the reason for choosing the case study strategy; the predominant use of questions that do not enable deeper analysis; many studies based on only one source of evidence; little use of data and information triangulation; little emphasis on the data collection method; a high number of cases in which confusion between case study as a research strategy and as data collection method were detected; a low number of papers reporting the method of data analysis; few reports on a study's contributions; and a minority highlighting the issues requiring further research. In conclusion, the method used to apply case studies to management accounting must be improved because few studies showed rigorous application of the procedures that this strategy requires.

scholarly journals Market Basket Analysis for Books Sales Promotion using FP Growth Algorithm, Case Study : Gramedia Matraman Jakarta

2021 ◽  
Vol 4 (2) ◽  
pp. 383-392
Author(s):  
Firmansyah Firmansyah ◽  
Agus Yulianto
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Sales Promotion ◽  
Frequent Pattern ◽  
Analysis Method ◽  
Analysis Framework ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Industry Standard

For retail companies such as Gramedia stores, promotion and strategies to sell books are important, so tools are needed to analyze past sales data. Gramedia does not yet have tools to analyze shopping cart patterns that aim to carry out product promotions appropriately. To promote what books should be promoted using the market basket analysis method or shopping basket analysis. The algorithm used in the data mining process is Frequent Pattern Growth (FP Growth) because it is faster in processing large data. The data analyzed is historical data on book sales from January to March 2020 which is taken randomly (random sampling). The framework used in the data mining process is the Cross Industry Standard Process for Data Mining (CRISP-DM) and the tool used is the Rapid Miner using a market basket analysis framework. With a minimum support of 0.003 and a minimum confidence 0.3 using the FP-Growth algorithm to produce an item set of 7 rules to recommend product promotions. The algorithm results are also in accordance with the business understanding phase of CRISP-DM.

scholarly journals Design and Implementation of Hybrid Algorithm for e-news Classification.

2013 ◽  
Vol 12 (1) ◽  
pp. 3178-3186
Author(s):  
Harneet Kaur ◽  
Kiran Jyoti
Keyword(s):  
Data Mining ◽  
Hybrid Algorithm ◽  
Intelligent System ◽  
Large Data ◽  
Online News ◽  
Data Sets ◽  
Online Newspapers ◽  
Use Of Data ◽  
Internet News ◽  
Use Of Internet

Data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. As the use of internet is increasing day by day and with the advancement of internet news also publish online. So to handle this bulk amount of news various data mining techniques for classification had been used. In this paper we are using an intelligent system based on Hybrid algorithm (HMM, SVM and CART) for e-news classification. An intelligent system is designed which will extract the online news and then will find out category and subcategory wise news. System involves four main stages: a) Keyword Extraction b) Implementation of Hybrid Algorithm (HMM, SVM and CART). Data have been collected for experimentation from online newspapers like The Hindu, Hindustan Times and Times of India. The experimental results are based on the news categories and sub categories such as Entertainment: Bollywood 100% and Hollywood 90%, Sports: Cricket 90%, Football 90% and Hockey 78%, Matrimonial :Hindu 100% and Muslim 80%. In this paper we also compare the result of Hybrid algorithm (HMM, SVM and CART) with individual HMM and SVM Algorithm and conclude that Hybrid algorithm (HMM, SVM and CART) gave better result than that of what HMM and SVM individually gave.

scholarly journals Application of Data Mining Method Using Association Rules Apriori To Shopping Cart Analysis On Sale Transactions (Case Study Alfamidi Burnt Stone)

2020 ◽  
Vol 2 (2) ◽  
pp. 222-226
Author(s):  
Vanessa Siregar ◽  
Paska Marto Hasugian
Keyword(s):  
Data Mining ◽  
Knowledge Discovery In Databases ◽  
Data Sets ◽  
Cart Analysis ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Large Size ◽  
Using Data ◽  
Mining Algorithms

Also Often data mining is called knowledge discovery in databases (KDD), ie activities include the collection, historical use of data to find regularities, patterns or relationships in data sets with a large size. The company may be interested to know if some groups consistently goods items purchased together. This study analyzes the transaction of data information retrieval from the sale of skin care and hair care using data mining algorithms priori Alfamidi Burnt Stones with the highest support value is 8% and the highest value is 5% confidance

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