Research on financial data analysis based on data mining algorithm

2018 ◽  
Vol 31 (10) ◽  
pp. e4780 ◽  
Author(s):  
Wei Yu ◽  
Shijun Li
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Financial Data ◽  
Data Mining Algorithm ◽  
Mining Algorithm ◽  
Financial Data Analysis

Secure Data Analysis in Clusters (Iris Database)

2017 ◽  
pp. 52-61
Author(s):  
Raghvendra Kumar ◽  
Prasant Kumar Pattnaik ◽  
Priyanka Pandey
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Privacy Preservation ◽  
Data Communication ◽  
Distributed Database ◽  
Privacy Preserving ◽  
Data Mining Algorithm ◽  
Data Set ◽  
Mining Algorithm ◽  
Secure Data

This chapter used privacy preservation techniques (Data Modification) to ensure Privacy. Privacy preservation is another important issue. A picture, where number of clients owning their clustered databases (Iris Database) wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information and requires the privacy of the privileged information. There are numbers of efficient protocols are required for privacy preserving in data mining. This chapter presented various privacy preserving protocols that are used for security in clustered databases. The Xln(X) protocol and the secure sum protocol are used in mutual computing, which can defend privacy efficiently. Its focuses on the data modification techniques, where it has been modified our distributed database and after that sanded that modified data set to the client admin for secure data communication with zero percentage of data leakage and also reduce the communication and computation complexity.

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>

scholarly journals A product-centric data mining algorithm for targeted promotions

2020 ◽  
Vol 54 ◽  
pp. 101940 ◽  
Author(s):  
Raymond Moodley ◽  
Francisco Chiclana ◽  
Fabio Caraffini ◽  
Jenny Carter
Keyword(s):  
Data Mining ◽  
Data Mining Algorithm ◽  
Mining Algorithm

scholarly journals Life-cycle Asset Management in Residential Developments Building on Transport System Critical Attributes via a Data-mining Algorithm

Buildings ◽  
2018 ◽  
Vol 9 (1) ◽  
pp. 1 ◽  
Author(s):  
Umair Hasan ◽  
Andrew Whyte ◽  
Hamad Al Jassmi
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Built Environment ◽  
Transport System ◽  
Asset Management ◽  
Public Transport ◽  
Categorical Variables ◽  
Data Mining Algorithm ◽  
Large Set ◽  
Mining Algorithm

Public transport can discourage individual car usage as a life-cycle asset management strategy towards carbon neutrality. An effective public transport system contributes greatly to the wider goal of a sustainable built environment, provided the critical transit system attributes are measured and addressed to (continue to) improve commuter uptake of public systems by residents living and working in local communities. Travel data from intra-city travellers can advise discrete policy recommendations based on a residential area or development’s public transport demand. Commuter segments related to travelling frequency, satisfaction from service level, and its value for money are evaluated to extract econometric models/association rules. A data mining algorithm with minimum confidence, support, interest, syntactic constraints and meaningfulness measure as inputs is designed to exploit a large set of 31 variables collected for 1,520 respondents, generating 72 models. This methodology presents an alternative to multivariate analyses to find correlations in bigger databases of categorical variables. Results here augment literature by highlighting traveller perceptions related to frequency of buses, journey time, and capacity, as a net positive effect of frequent buses operating on rapid transit routes. Policymakers can address public transport uptake through service frequency variation during peak-hours with resultant reduced car dependence apt to reduce induced life-cycle environmental burdens of buildings by altering residents’ mode choices, and a potential design change of buildings towards a public transit-based, compact, and shared space urban built environment.

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