scholarly journals A review of data mining methods in financial markets

2021 ◽  
Vol 1 (4) ◽  
pp. 362-392
Author(s):  
Haihua Liu ◽  
◽  
Shan Huang ◽  
Peng Wang ◽  
Zejun Li ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Financial Markets ◽  
Social Life ◽  
Development Trend ◽  
Financial Data ◽  
Support Vector ◽  
Mining Methods ◽  
Financial Data Analysis ◽  
Using Data

<abstract><p>Financial activities are closely related to human social life. Data mining plays an important role in the analysis and prediction of financial markets, especially in the context of the current era of big data. However, it is not simple to use data mining methods in the process of analyzing financial data, due to the differences in the background of researchers in different disciplines. This review summarizes several commonly used data mining methods in financial data analysis. The purpose is to make it easier for researchers in the financial field to use data mining methods and to expand the application scenarios of it used by researchers in the computer field. This review introduces the principles and steps of decision trees, support vector machines, Bayesian, K-nearest neighbors, k-means, Expectation-maximization algorithm, and ensemble learning, and points out their advantages, disadvantages and applicable scenarios. After introducing the algorithms, it summarizes the use of the algorithm in the process of financial data analysis, hoping that readers can get specific examples of using the algorithm. In this review, the difficulties and countermeasures of using data mining methods are summarized, and the development trend of using data mining methods to analyze financial data is predicted.</p></abstract>

Early Breast Cancer Prediction Using Dermatoglyphics: Data Mining Pilot Study in a General Hospital in Iran (Preprint)

2020 ◽  
Author(s):  
Seyed Mohammad Ayyoubzadeh ◽  
Aysan Almasizand ◽  
Sharareh R. Niakan Kalhori ◽  
Sakineh Abbasi
Keyword(s):  
Breast Cancer ◽  
Data Mining ◽  
Random Forest ◽  
Early Breast Cancer ◽  
Information Gain ◽  
Support Vector ◽  
Mining Methods ◽  
Using Data ◽  
Birth Family ◽  
Hands And Feet

BACKGROUND Dermatoglyphics is the study of skin patterns on hands and feet. It has been shown in some studies that specific finger patterns could be a risk factor of breast cancer. There are several studies using data mining methods to evaluate the risk of breast cancer; while there is no or little study that evaluates finger patterns with data mining for breast cancer risk prediction. OBJECTIVE This study aims to evaluate fingerprint patterns along with other easy-to-obtain features in the risk of breast cancer. METHODS A dataset containing 462 records includes female patients in Imam Khomeini Hospital Complex, Tehran, Iran was obtained. The dataset has comprised of age, menstruation age, menopause age, and situation, has a child, age at first live birth, family history of breast cancer, and figure print patterns features of hands. The factors weight was determined by the Information Gain index. Predictive models were built once without fingerprint features and once with fingerprint features using Naïve Bayes, Decision Tree, Random Forest (RF), Support Vector Machine (SVM), and Deep Learning classifiers. RESULTS The most important factor determining breast cancer were age, having a child, menopause situation, and menopause age. The best performance belongs to the RF model with accuracy and AUC of 84.43% and 0.923 respectively. The fingerprint patterns feature increased the RF accuracy from 79.44% to 84.43%. CONCLUSIONS An early breast cancer screening model could be built with the use of data mining methods. The fingerprint patterns could increase the performance of these models. The Random Forest model could be used. The results of such models could be used in designing apps for self-screening breast cancer.

The Application of Data Mining Technology in the Data Analysis of Traditional Chinese Medicine

2014 ◽  
Vol 687-691 ◽  
pp. 1266-1269
Author(s):  
Zhen Wang ◽  
Kan Kan She
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Analysis ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Rapid Development ◽  
Theoretical Knowledge ◽  
Mining Technology ◽  
Mining Methods ◽  
Using Data

With the rapid development of information technology, the amount of data accumulated by people is increasing sharply. Data mining technology is an effective method to find useful information from vast amounts of data and increase the utilization of information. After thousands of years of development, traditional Chinese medicine has accumulated a wealth of theoretical knowledge and a lot of books and records, more and more Chinese medicine databases are created. Using data mining technology to mine the unknown knowledge and rules and put forward assumptions for experiment and theory can be a good auxiliary research of traditional Chinese medicine. This article analyzes the data mining methods of traditional Chinese medicine at first. Then, the application of data mining technology in traditional Chinese medicine data analysis is introduced which includes the data mining of traditional Chinese medicine literatures, diagnosis and clinic of traditional Chinese medicine and prescription and medication of traditional Chinese medicine. At last, the aspects which need to be paid attention to in the data mining of traditional Chinese medicine are pointed out.

Analysis of Heart Disorder by Using Machine Learning Methods and Data Mining Techniques

2021 ◽  
pp. 212-221
Author(s):  
Sarangam Kodati ◽  
Jeeva Selvaraj
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Support Vector Machine ◽  
Heart Disease ◽  
Heart Diseases ◽  
Support Vector ◽  
Primary Reason ◽  
The World ◽  
Mining Methods ◽  
Using Data

Data mining is the most famous knowledge extraction approach for knowledge discovery from data (KDD). Machine learning is used to enable a program to analyze data, recognize correlations, and make usage on insights to solve issues and/or enrich data and because of prediction. The chapter highlights the need for more research within the usage of robust data mining methods in imitation of help healthcare specialists between the diagnosis regarding heart diseases and other debilitating disease conditions. Heart disease is the primary reason of death of people in the world. Nearly 47% of death is caused by heart disease. The authors use algorithms including random forest, naïve Bayes, support vector machine to analyze heart disease. Accuracy on the prediction stage is high when using a greater number of attributes. The goal is to function predictive evaluation using data mining, using data mining to analyze heart disease, and show which methods are effective and efficient.

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

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