scholarly journals Analysis of the cryptocurrency market using different prototype-based clustering techniques

2022 ◽  
Vol 8 (1) ◽  
Author(s):  
Luis Lorenzo ◽  
Javier Arroyo
Keyword(s):  
Financial Markets ◽  
Clustering Algorithms ◽  
Extended Period ◽  
Financial Behavior ◽  
Financial Ratios ◽  
Future Research ◽  
Clustering Techniques ◽  
Fine Grained ◽  
Partitional Clustering ◽  
Using Data

AbstractSince the emergence of Bitcoin, cryptocurrencies have grown significantly, not only in terms of capitalization but also in number. Consequently, the cryptocurrency market can be a conducive arena for investors, as it offers many opportunities. However, it is difficult to understand. This study aims to describe, summarize, and segment the main trends of the entire cryptocurrency market in 2018, using data analysis tools. Accordingly, we propose a new clustering-based methodology that provides complementary views of the financial behavior of cryptocurrencies, and one that looks for associations between the clustering results, and other factors that are not involved in clustering. Particularly, the methodology involves applying three different partitional clustering algorithms, where each of them use a different representation for cryptocurrencies, namely, yearly mean, and standard deviation of the returns, distribution of returns that have not been applied to financial markets previously, and the time series of returns. Because each representation provides a different outlook of the market, we also examine the integration of the three clustering results, to obtain a fine-grained analysis of the main trends of the market. In conclusion, we analyze the association of the clustering results with other descriptive features of cryptocurrencies, including the age, technological attributes, and financial ratios derived from them. This will help to enhance the profiling of the clusters with additional descriptive insights, and to find associations with other variables. Consequently, this study describes the whole market based on graphical information, and a scalable methodology that can be reproduced by investors who want to understand the main trends in the market quickly, and those that look for cryptocurrencies with different financial performance.In our analysis of the 2018 and 2019 for extended period, we found that the market can be typically segmented in few clusters (five or less), and even considering the intersections, the 6 more populations account for 75% of the market. Regarding the associations between the clusters and descriptive features, we find associations between some clusters with volume, market capitalization, and some financial ratios, which could be explored in future research.

scholarly journals Analysis of the cryptocurrency market applying different prototype-based clustering techniques

2021 ◽  
Author(s):  
Luis Lorenzo
Keyword(s):  
Data Analysis ◽  
Standard Deviation ◽  
Financial Performance ◽  
Financial Ratios ◽  
Clustering Techniques ◽  
Fine Grained ◽  
Partitional Clustering ◽  
Analysis Tools

Since the appearance of Bitcoin, cryptocurrencies have experienced enormousgrowth not only in terms of capitalization but also in number. As a result, thecryptocurrency market can be an attractive arena for investors as it offers manypossibilities, but a difficult one to understand as well. In this work, we aim tosummarize and segment the whole cryptocurrency market in 2018 with the helpof data analysis tools. We will use three different partitional clustering algorithmseach of them using a different representation for cryptocurrencies, namely: yearlymean and standard deviation of the returns, distribution of returns, and timeseries of returns. Since each representation will provide a different andcomplementary perspective of the market, we will also explore the combination ofthe three clustering results to obtain a fine-grained analysis of the main trends ofthe market. Finally, we will analyse the association of the clustering results withother descriptive features of the cryptocurrencies, including the age, technologicalattributes, and financial ratios derived from them. This will help to enhance theprofiling of the clusters with additional insights. As a result, this work offers adescription of the market and a methodology that can be reproduced by investorsthat want to understand the main trends on the market and that look forcryptocurrencies with different financial performance.

A Study on Efficient Clustering Techniques Involved in Dealing With Diverse Attribute Data

2020 ◽  
pp. 131-149
Author(s):  
Pragathi Penikalapati ◽  
A. Nagaraja Rao
Keyword(s):  
State Of The Art ◽  
Clustering Algorithms ◽  
Mixed Data ◽  
Future Research ◽  
Research Directions ◽  
Clustering Techniques ◽  
Attribute Data ◽  
Unlabelled Data ◽  
Future Research Directions ◽  
Categorical Attributes

The compatibility issues among the characteristics of data involving numerical as well as categorical attributes (mixed) laid many challenges in pattern recognition field. Clustering is often used to group identical elements and to find structures out of data. However, clustering categorical data poses some notable challenges. Particularly clustering diversified (mixed) data constitute bigger challenges because of its range of attributes. Computations on such data are merely too complex to match the scales of numerical and categorical values due to its ranges and conversions. This chapter is intended to cover literature clustering algorithms in the context of mixed attribute unlabelled data. Further, this chapter will cover the types and state of the art methodologies that help in separating data by satisfying inter and intracluster similarity. This chapter further identifies challenges and Future research directions of state-of-the-art clustering algorithms with notable research gaps.

scholarly journals A Theoretical Evaluation of Mellitus Diabetes using Data Mining and Machine Learning

2021 ◽  
pp. 612-620
Author(s):  
Shivani Patel ◽  
Sanjay Chaudhary ◽  
Prakashsingh Tanwar
Keyword(s):  
Data Mining ◽  
Extended Period ◽  
Medical Diagnostics ◽  
Future Research ◽  
Theoretical Evaluation ◽  
Research Directions ◽  
Future Research Directions ◽  
Using Data ◽  
Sugar Levels ◽  
Gathering Data

Pattern identification, processing, and treatment are all common uses of data mining techniques in medical diagnostics. Diabetes is a metabolic illness in which elevated blood sugar levels persist for an extended period of time. Diabetes mellitus (DM) is a collection of metabolic illnesses that puts a lot of pressure on people all over the world. According to these studies, India accounts for 19% of the world's residents. Category 1 and Category 2 diabetes are covered in this overview. Theoretical basis is used to compare previous researcher methodologies and processes. To process datasets, the Weka open-source tool is employed. In the first half, we'll talk about gathering data from various medical departments; in the second part, we'll talk about data cleaning and then algorithms for removing noisy data. Also, several Algorithms were used to determine the best characteristic. Finally, we'll look at alternative machine learners for diabetes data classification and discuss future research directions.

scholarly journals Performance Comparison with Hierarchical and Partitional Clustering Methods

2021 ◽  
Vol 20 ◽  
pp. 177-184
Author(s):  
Ozer Ozdemir ◽  
Simgenur Cerman
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Performance Comparison ◽  
Hierarchical Partitioning ◽  
Clustering Methods ◽  
Data Set ◽  
Partitional Clustering ◽  
Statistical Programming ◽  
Using Data

In data mining, one of the commonly-used techniques is the clustering. Clustering can be done by the different algorithms such as hierarchical, partitioning, grid, density and graph based algorithms. In this study first of all the concept of data mining explained, then giving information the aims of using data mining and the areas of using and then clustering and clustering algorithms that used in data mining are explained theoretically. Ultimately within the scope of this study, "Mall Customers" data set that taken from Kaggle database, based partitioned clustering and hierarchical clustering algorithms aimed at the separation of clusters according to their costumers features. In the clusters obtained by the partitional clustering algorithms, the similarity within the cluster is maximum and the similarity between the clusters is minimum. The hierarchical clustering algorithms is based on the gathering of similar features or vice versa. The partitional clustering algorithms used; k-means and PAM, hierarchical clustering algorithms used; AGNES and DIANA are algorithms. In this study, R statistical programming language was used in the application of algorithms. At the end of the study, the data set was run with clustering algorithms and the obtained analysis results were interpreted.

scholarly journals A pragmatic view on the financial theories

2018 ◽  
Vol 12 (1) ◽  
pp. 197-208
Author(s):  
Mihai Chișu
Keyword(s):  
Financial Markets ◽  
Nobel Prize ◽  
Investment Decision ◽  
Efficient Market Hypothesis ◽  
Daily Basis ◽  
Nobel Prize Winner ◽  
Financial Behavior ◽  
Future Research ◽  
John Maynard Keynes ◽  
Huge Data

Abstract This presentation reviews some real examples from a trading daily basis behavior proving the sentiment is one of the most important drivers when it comes to investment decision. During decades of studying and observing the financial markets we have seen different approaches in the light of many prestigious writers. Are we rational enough to be good candidates for Fama’s theory of Efficient Market Hypothesis? Is it true what John Maynard Keynes stated 90 years ago when he said „the market is subject to waves of optimistic and pessimistic sentiment”? Is the financial behavior the new trend in the financial markets? Are Daniel Kahneman (Nobel Prize winner 2002) and Amos Tversky the new challengers in the market theories league? Future research should concentrate on various symptoms of sentiment and what makes investors become prone to sentiment. This is an important issue to be debated since investors constantly have to analyze, process and interpret huge data of information which provides the basis for their actions.

Workplace Ostracism and Its Negative Outcomes

2016 ◽  
Vol 15 (4) ◽  
pp. 143-151 ◽  
Author(s):  
Xiaoming Zheng ◽  
Jun Yang ◽  
Hang-Yue Ngo ◽  
Xiao-Yu Liu ◽  
Wengjuan Jiao
Keyword(s):  
Affective Commitment ◽  
Psychological Capital ◽  
Intention To Leave ◽  
Future Research ◽  
Conservation Of Resources ◽  
Negative Consequences ◽  
Negative Outcomes ◽  
Workplace Ostracism ◽  
Using Data ◽  
The Relationship

Abstract. Workplace ostracism, conceived as to being ignored or excluded by others, has attracted the attention of researchers in recent years. One essential topic in this area is how to reduce or even eliminate the negative consequences of workplace ostracism. Based on conservation of resources (COR) theory, the current study assesses the relationship between workplace ostracism and its negative outcomes, as well as the moderating role played by psychological capital, using data collected from 256 employees in three companies in the northern part of China. The study yields two important findings: (1) workplace ostracism is positively related to intention to leave and (2) psychological capital moderates the effect of workplace ostracism on affective commitment and intention to leave. This paper concludes by discussing the implications of these findings for organizations and employees, along with recommendations for future research.

Siblings and Illness

2011 ◽  
Vol 7 ◽  
Author(s):  
Leah Sawyer Vanderwerp
Keyword(s):  
Depressive Symptoms ◽  
Birth Order ◽  
Family Size ◽  
Multivariate Analyses ◽  
Social Role ◽  
Chronically Ill ◽  
Demographic Variables ◽  
Future Research ◽  
Mother And Child ◽  
Using Data

Using data from the National Longitudinal Survey of Youth-Mother and Child samples, I investigated the relationships among child and adolescent depressive symptoms, having a chronically ill sibling, and other child and familial demographic variables. From research on social support and social role transitions, with the Stress Process as a theoretical model, I hypothesized that children with chronically ill siblings experience more depressive symptoms. Specifically, I looked at age, gender, birth order and family size as potentially reducing the effect size of having a chronically ill sibling. Findings showed that having a chronically ill sibling is associated with demonstrating more depressive symptoms both in the bivariate and multivariate analyses. Although age, gender, birth order and family size do not interact significantly with having a chronically ill sibling in predicting depressive symptoms, they do present interesting findings about childhood depressive symptoms in general. Thus, the results of this study suggest specific and meaningful paths for future research.

scholarly journals Evaluating the Relationship Between Initial Injury, Referral to A Pain Clinic, and Medical Retirement from the Army: A Retrospective Analysis

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 502-505
Author(s):  
Justin J Stewart ◽  
Diane Flynn ◽  
Alana D Steffen ◽  
Dale Langford ◽  
Honor McQuinn ◽  
...  
Keyword(s):  
Chronic Pain ◽  
Retrospective Analysis ◽  
Extended Period ◽  
Pain Clinic ◽  
Outcome Variable ◽  
Future Research ◽  
Curvilinear Relationship ◽  
The Impact ◽  
Pain Care ◽  
The Relationship

ABSTRACT Introduction Soldiers are expected to deploy worldwide and must be medically ready in order to accomplish their mission. Soldiers unable to deploy for an extended period of time because of chronic pain or other conditions undergo an evaluation for medical retirement. A retrospective analysis of existing longitudinal data from an Interdisciplinary Pain Management Center (IPMC) was used to evaluate the temporal relationship between the time of initial duty restriction and referral for comprehensive pain care to being evaluated for medical retirement. Methods Patients were adults (>18 years old) and were cared for in an IPMC at least once between May 1, 2014 and February 28, 2018. A total of 1,764 patients were included in the final analysis. Logistic regression was used to evaluate the impact of duration between date of first duty restriction documentation and IPMC referral to the outcome variable of establishment of a permanent 3 (P3) profile. Results The duration between date of first duty restriction and IPMC referral showed a curvilinear relationship to probability of a P3 profile. According to our model, a longer duration before referral is associated with an increased probability of a subsequent P3 profile with the highest probability peaking at 19 months. The probability of P3 declines gradually for those who were referred later. Discussion This is the first time the relationship between time of initial duty restriction, referral to an IPMC, and subsequent P3 or higher profile has been tested. Future research is needed to examine medical conditions listed on the profile to see how they might contribute to the cause of referral to the IPMC. Conclusion A longer duration between initial duty restriction and referral to IPMC was associated with higher odds of subsequent P3 status for up to 19 months. Referral to an IPMC for comprehensive pain care early in the course of chronic pain conditions may reduce the likelihood of P3 profile and eventual medical retirement of soldiers.

scholarly journals Adopting the Situation in School Questionnaire to Examine Physical Education Teachers’ Motivating and Demotivating Styles Using a Circumplex Approach

2021 ◽  
Vol 18 (14) ◽  
pp. 7342
Author(s):  
Géraldine Escriva-Boulley ◽  
Emma Guillet-Descas ◽  
Nathalie Aelterman ◽  
Maarten Vansteenkiste ◽  
Nele Van Doren ◽  
...  
Keyword(s):  
Physical Education ◽  
Autonomy Support ◽  
Future Research ◽  
Physical Education Teachers ◽  
Structure Control ◽  
Fine Grained ◽  
Circular Structure ◽  
Need Support ◽  
Academic Courses

Grounded in SDT, several studies have highlighted the role of teachers’ motivating and demotivating styles for students’ motivation, learning, and physical activity in physical education (PE). However, most of these studies focused on a restricted number of motivating strategies (e.g., offering choice) or dimensions (e.g., autonomy support). Recently, researchers have developed the Situations-in-School (i.e., SIS-Education) questionnaire, which allows one to gain a more integrative and fine-grained insight into teachers’ engagement in autonomy-support, structure, control, and chaos through a circular structure (i.e., a circumplex). Although teaching in PE resembles teaching in academic courses in many ways, some of the items of the original situation-based questionnaire (e.g., regarding homework) are irrelevant to the PE context. In the present study, we therefore sought to develop a modified, PE-friendly version of this earlier validated SIS-questionnaire—the SIS-PE. Findings in a sample of Belgian (N = 136) and French (N = 259) PE teachers, examined together and as independent samples, showed that the variation in PE teachers’ motivating styles in this adapted version is also best captured by a circumplex structure, with four overarching styles and eight subareas differing in their level of need support and directiveness. The SIS-PE possesses excellent convergent and concurrent validity. With the adaptations being successful, great opportunities for future research on PE teachers (de-)motivating styles are created.

scholarly journals Affinity Selection in Germinal Centers: Cautionary Tales and New Opportunities

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1040
Author(s):  
Jose Faro ◽  
Mario Castro
Keyword(s):  
T Cell ◽  
B Cell ◽  
Cell Receptor ◽  
Future Research ◽  
Mathematical Framework ◽  
Immune Synapse ◽  
Cell Receptors ◽  
3 Dimensional ◽  
Fine Grained ◽  
Ligand Interactions

Our current quantitative knowledge of the kinetics of antibody-mediated immunity is partly based on idealized experiments throughout the last decades. However, new experimental techniques often render contradictory quantitative outcomes that shake previously uncontroversial assumptions. This has been the case in the field of T-cell receptors, where recent techniques for measuring the 2-dimensional rate constants of T-cell receptor–ligand interactions exposed results contradictory to those obtained with techniques measuring 3-dimensional interactions. Recently, we have developed a mathematical framework to rationalize those discrepancies, focusing on the proper fine-grained description of the underlying kinetic steps involved in the immune synapse. In this perspective article, we apply this approach to unveil potential blind spots in the case of B-cell receptors (BCR) and to rethink the interactions between B cells and follicular dendritic cells (FDC) during the germinal center (GC) reaction. Also, we elaborate on the concept of “catch bonds” and on the recent observations that B-cell synapses retract and pull antigen generating a “retracting force”, and propose some testable predictions that can lead to future research.

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