scholarly journals Dual water choices: The assessment of the influential factors on water sources choices using unsupervised machine learning market basket analysis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Tiyasha Tiyasha ◽  
Suraj Kumar Bhagat ◽  
Firaol Fituma ◽  
Tran Minh Tung ◽  
Shamsuddin Shahid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Sources ◽  
Influential Factors ◽  
Market Basket Analysis ◽  
Unsupervised Machine Learning ◽  
Market Basket

scholarly journals A new optimization model for market basket analysis with allocation considerations: A genetic algorithm solution approach

2017 ◽  
Vol 12 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Majeed Heydari ◽  
Amir Yousefli
Keyword(s):  
Genetic Algorithm ◽  
Optimization Model ◽  
Heuristic Algorithms ◽  
Influential Factors ◽  
Market Basket Analysis ◽  
Binary Programming ◽  
Modeling And Analysis ◽  
Market Basket ◽  
Solution Approach ◽  
Research Areas

Abstract Nowadays market basket analysis is one of the interested research areas of the data mining that has received more attention by researchers. But, most of the related research focused on the traditional and heuristic algorithms with limited factors that are not the only influential factors of the basket market analysis. In this paper to efficient modeling and analysis of the market basket data, the optimization model is proposed with considering allocation parameter as one of the important and effectual factors of the selling rate. The genetic algorithm approach is applied to solve the formulated non-linear binary programming problem and a numerical example is used to illustrate the presented model. The provided results reveal that the obtained solutions seem to be more realistic and applicable.

Intelligent Slotting for the Warehouse

2016 ◽  
pp. 326-342
Author(s):  
Narayanan Padmanabahan Iyer ◽  
Ramesh Jayal
Keyword(s):  
Machine Learning ◽  
Supply Chain ◽  
Business Growth ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Rapid Changes ◽  
Future Supply

In the current and future Supply Chain landscape, we need to ensure that we keep the warehouse ship sailing amidst the turbulent waters of dynamic business growth and rapid changes in technology. There are several challenges to be overcome, and many opportunities to be embraced on the path to achieving this. In this chapter, the authors detail one of the key problems facing the warehouse and that is Slotting. They look at the various business drivers, and technological drivers impacting Slotting. They propose a solution to tackle this problem by using Market Basket Analysis and Machine Learning.

Market Basket Analysis Approach to Machine Learning

2021 ◽  
Author(s):  
Abu Hasnat Patwary ◽  
Md Tamim Eshan ◽  
Prazzal Debnath ◽  
Abdus Sattar
Keyword(s):  
Machine Learning ◽  
Analysis Approach ◽  
Market Basket Analysis ◽  
Market Basket

scholarly journals Approach to Kirana Store Product Arrangement Using Machine Learning

2021 ◽  
pp. 72-83
Author(s):  
Ajita Patel ◽  
Krishna Kumar Tiwari
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Domain Knowledge ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Detection Techniques ◽  
Store Product ◽  
Shopping Basket ◽  
Objective Detection ◽  
Better Than

Market Basket Analysis (MBA) is a method for determining the association between entities, and it has often been used to study the association between products in a shopping basket. Trained Computer vision models are able to recognize objects in photos so accurately that it can even outperform humans in some instances. This study shows that combining objective detection techniques with market basket analysis can assist Stores/Kirana in organizing the products effectively. With the use of MBA and Object detection, we formulated recommendations for store arrangements along with putting a recommendation engine on top to help shoppers. After deploying this to local Kirana stores, the Kirana store was able to see an increase of 7% in the sale. The recommendation engine performed better than just the domain knowledge of the kirana store.

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

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

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