FCA-Based Data Analysis for Discovering Association Rules in Social Network Service

2015 ◽  
Vol 764-765 ◽  
pp. 910-914
Author(s):  
Jeong Dong Kim ◽  
Suk Hyung Hwang ◽  
Doo Kwon Baik
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Association Rules ◽  
Formal Concept Analysis ◽  
Methodological Approach ◽  
Mining Machine ◽  
Formal Concept ◽  
Data Sets ◽  
Social Network Service ◽  
Conceptual Structures

Recently, Formal Concept Analysis (FCA) have been widely used for various purposes in many different domains such as data mining, machine learning, knowledge management and so on. In this paper, we introduce FCA as the basis for a practical and well founded methodological approach for data analysis which identifies conceptual structures among data sets. As well as, we propose a FCA-based data analysis for discovering association rules by using polarity from social contents. Additionally, we show the experiments that demonstrate how our data analysis approaches can be applied for knowledge discovery by using association rules.

scholarly journals An Incremental Algorithm for Concept Lattice Based on SSIM

2021 ◽  
Author(s):  
Yu Hu ◽  
Yan Zhu Hu ◽  
Zhong Su ◽  
Xiao Li Li ◽  
Zhen Meng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Formal Concept Analysis ◽  
Time Complexity ◽  
Complexity Analysis ◽  
Concept Lattice ◽  
Incremental Algorithm ◽  
Formal Concept ◽  
Image Structure ◽  
Structure Similarity

Abstract As an effective tool for data analysis, Formal Concept Analysis (FCA) is widely used in software engineering and machine learning. The construction of concept lattice is a key step of the FCA. How to effectively update the concept lattice is still an open, interesting and important issue. The main aim of this paper is to provide a solution to this problem. So, we propose an incremental algorithm for concept lattice based on image structure similarity (SsimAddExtent). In addition, we perform time complexity analysis and experiments to show effectiveness of algorithm.

scholarly journals A Novel Concept Acquisition Approach Based on Formal Contexts

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ting Qian ◽  
Ling Wei
Keyword(s):  
Data Analysis ◽  
Formal Concept Analysis ◽  
Real Life ◽  
Experimental Results ◽  
New Method ◽  
Concept Acquisition ◽  
Formal Concept ◽  
Knowledge Processing ◽  
Formal Concepts ◽  
Novel Concept

As an important tool for data analysis and knowledge processing, formal concept analysis (FCA) has been applied to many fields. In this paper, we introduce a new method to find all formal concepts based on formal contexts. The amount of intents calculation is reduced by the method. And the corresponding algorithm of our approach is proposed. The main theorems and the corresponding algorithm are examined by examples, respectively. At last, several real-life databases are analyzed to demonstrate the application of the proposed approach. Experimental results show that the proposed approach is simple and effective.

Association Analysis of Alumni Giving

2011 ◽  
pp. 262-274
Author(s):  
Ray R. Hashemi ◽  
Louis A. Le Blanc ◽  
Azita A. Bahrami ◽  
Mahmood Bahar ◽  
Bryan Traywick
Keyword(s):  
Association Analysis ◽  
Association Rules ◽  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Fund Raising ◽  
Alumni Giving ◽  
Formal Concept ◽  
Non Profit ◽  
Major Gifts ◽  
The Gift

A large sample (initially 33,000 cases representing a ten percent trial) of university alumni giving records for a large public university in the southwestern United States is analyzed by Formal Concept Analysis. This likely represents the initial attempt to perform analysis of such data by means of a machine learning technique. The variables employed include the gift amount to the university foundation as well as traditional demographic variables such as year of graduation, gender, ethnicity, marital status, etc. The foundation serves as one of the institution’s non-profit, fund-raising organizations. It pursues substantial gifts that are designated for the educational or leadership programs of the giver’s choice. Although they process gifts of all sizes, the foundation’s focus is on major gifts and endowments. Association Analysis of the given dataset is a two-step process. In the first step, FCA is applied to identify concepts and their relationships and in the second step, the association rules are defined for each concept. The hypothesis examined in this paper is that the generosity of alumni toward his/her alma mater can be predicted using association rules obtained by applying the Formal Concept Analysis approach.

Rough Set Analysis and Formal Concept Analysis

2011 ◽  
pp. 108-127
Author(s):  
Yiyu Yao
Keyword(s):  
Data Analysis ◽  
Comparative Study ◽  
Rough Set ◽  
Formal Concept Analysis ◽  
Concept Analysis ◽  
Formal Concept ◽  
Intelligent Data Analysis ◽  
Unified Framework

Rough set analysis (RSA) and formal concept analysis (FCA) are two theories of intelligent data analysis. They can be compared, combined and applied to each other. In this chapter, we review the existing studies on the comparisons and combinations of rough set analysis and formal concept analysis and report some new results. A comparative study of two theories in a unified framework provides a better understanding of data analysis.

A Comparison of Machine Learning Algorithms of Big Data for Time Series Forecasting Using Python

2020 ◽  
pp. 197-218
Author(s):  
Son Nguyen ◽  
Anthony Park
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Time Series ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Time Series Forecasting ◽  
Machine Learning Algorithms ◽  
Time Series Models ◽  
Data Sets

This chapter compares the performances of multiple Big Data techniques applied for time series forecasting and traditional time series models on three Big Data sets. The traditional time series models, Autoregressive Integrated Moving Average (ARIMA), and exponential smoothing models are used as the baseline models against Big Data analysis methods in the machine learning. These Big Data techniques include regression trees, Support Vector Machines (SVM), Multilayer Perceptrons (MLP), Recurrent Neural Networks (RNN), and long short-term memory neural networks (LSTM). Across three time series data sets used (unemployment rate, bike rentals, and transportation), this study finds that LSTM neural networks performed the best. In conclusion, this study points out that Big Data machine learning algorithms applied in time series can outperform traditional time series models. The computations in this work are done by Python, one of the most popular open-sourced platforms for data science and Big Data analysis.

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