Annals of Data Science 

About: Annals of Data Science is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Estimator & Computer science. It has an ISSN identifier of 2198-5804. Over the lifetime, 457 publications have been published receiving 4995 citations.

A Comprehensive Survey of Clustering Algorithms

Abstract: Data analysis is used as a common method in modern science research, which is across communication science, computer science and biology science. Clustering, as the basic composition of data analysis, plays a significant role. On one hand, many tools for cluster analysis have been created, along with the information increase and subject intersection. On the other hand, each clustering algorithm has its own strengths and weaknesses, due to the complexity of information. In this review paper, we begin at the definition of clustering, take the basic elements involved in the clustering process, such as the distance or similarity measurement and evaluation indicators, into consideration, and analyze the clustering algorithms from two perspectives, the traditional ones and the modern ones. All the discussed clustering algorithms will be compared in detail and comprehensively shown in Appendix Table 22.

Internet of Things, Real-Time Decision Making, and Artificial Intelligence

Abstract: In several earlier papers, the author defined and detailed the concept of a servgood, which can be thought of as a physical good or product enveloped by a services-oriented layer that makes the good smarter or more adaptable and customizable for a particular use Adding another layer of physical sensors could then enhance its smartness and intelligence, especially if it were to be connected with other servgoods—thus, constituting an Internet of Things (IoT) or servgoods More importantly, real-time decision making is central to the Internet of Things; it is about decision informatics and embraces the advanced technologies of sensing (ie, Big Data), processing (ie, real-time analytics), reacting (ie, real-time decision-making), and learning (ie, deep learning) Indeed, real-time decision making (RTDM) is becoming an integral aspect of IoT and artificial intelligence (AI), including its improving abilities at voice and video recognition, speech and predictive synthesis, and language and social-media understanding These three key and mutually supportive technologies—IoT, RTDM, and AI—are considered herein, including their progress to date

A Comprehensive Survey of Loss Functions in Machine Learning

Abstract: As one of the important research topics in machine learning, loss function plays an important role in the construction of machine learning algorithms and the improvement of their performance, which has been concerned and explored by many researchers. But it still has a big gap to summarize, analyze and compare the classical loss functions. Therefore, this paper summarizes and analyzes 31 classical loss functions in machine learning. Specifically, we describe the loss functions from the aspects of traditional machine learning and deep learning respectively. The former is divided into classification problem, regression problem and unsupervised learning according to the task type. The latter is subdivided according to the application scenario, and here we mainly select object detection and face recognition to introduces their loss functions. In each task or application, in addition to analyzing each loss function from formula, meaning, image and algorithm, the loss functions under the same task or application are also summarized and compared to deepen the understanding and provide help for the selection and improvement of loss function.

Forecasting with Big Data: A Review

Abstract: Big Data is a revolutionary phenomenon which is one of the most frequently discussed topics in the modern age, and is expected to remain so in the foreseeable future. In this paper we present a comprehensive review on the use of Big Data for forecasting by identifying and reviewing the problems, potential, challenges and most importantly the related applications. Skills, hardware and software, algorithm architecture, statistical significance, the signal to noise ratio and the nature of Big Data itself are identified as the major challenges which are hindering the process of obtaining meaningful forecasts from Big Data. The review finds that at present, the fields of Economics, Energy and Population Dynamics have been the major exploiters of Big Data forecasting whilst Factor models, Bayesian models and Neural Networks are the most common tools adopted for forecasting with Big Data.

How Many Judges Should There Be in a Group

Abstract: This paper briefly examines the question of how many judges are needed to obtain valid and consistent judgments when using the analytic hierarchy process. It turns out that if a judge is experienced and well versed in an area, he can be sufficient to provide the judgments instead of diluting his accuracy with the participation of others who may not be as good. How to discover such a person requires criteria used to judge his adequacy and that of others.