Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining - Concepts and Techniques.

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

https://opus.lib.uts.edu.au/bitstream/10453/114834/4/OCC-90253_AM.pdf

A review of clustering techniques and developments

Time-series clustering - A decade review

Similarity or distance measures are core components used by distance-based clustering algorithms to cluster similar data points into the same clusters, while dissimilar or distant data points are placed into different clusters. The performance of similarity measures is mostly addressed in two or three-dimensional spaces, beyond which, to the best of our knowledge, there is no empirical study that has revealed the behavior of similarity measures when dealing with high-dimensional datasets. To fill this gap, a technical framework is proposed in this study to analyze, compare and benchmark the influence of different similarity measures on the results of distance-based clustering algorithms. For reproducibility purposes, fifteen publicly available datasets were used for this study, and consequently, future distance measures can be evaluated and compared with the results of the measures discussed in this work. These datasets were classified as low and high-dimensional categories to study the performance of each measure against each category. This research should help the research community to identify suitable distance measures for datasets and also to facilitate a comparison and evaluation of the newly proposed similarity or distance measures with traditional ones.

/pdf/a-comparison-study-on-similarity-and-dissimilarity-measures-53pxkl21sb.pdf

A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data

Clustering is an essential data mining and tool for analyzing big data. There are difficulties for applying clustering techniques to big data duo to new challenges that are raised with big data. As Big Data is referring to terabytes and petabytes of data and clustering algorithms are come with high computational costs, the question is how to cope with this problem and how to deploy clustering techniques to big data and get the results in a reasonable time. This study is aimed to review the trend and progress of clustering algorithms to cope with big data challenges from very first proposed algorithms until today’s novel solutions. The algorithms and the targeted challenges for producing improved clustering algorithms are introduced and analyzed, and afterward the possible future path for more advanced algorithms is illuminated based on today’s available technologies and frameworks.

Big Data Clustering: A Review

Clustering is an unsupervised machine learning method that is used both individually and as a part of the preprocessing stage for the supervised machine learning methods. Due to its unsupervised nature, clustering results have less accuracy compared to the supervised learning. This article aims to introduce a new perspective in clustering by defining an approach for data pruning. The method also enables clustering using multiple sets of prototypes instead of only one set to improve clustering accuracy. Consequently, this approach has the potential to be used independently or as a part of a preprocessing to prepare purified data for the training step of a supervised learning approach. An evolving fuzzy clustering approach (EFCA) utilizes the fuzzy membership concept to breakdown clustering in epochs instead of running the clustering on all data at once. In some cases, for supervised learning, we rather have a smaller subset of highly accurate labeled data instead of a dataset with less accurate labels. The EFCA's “epoch cut” enables postpruning ability to eliminate obscure data points, which results in more clustering accuracy. The EFCA has been applied to a set of eight multivariate and ten time-series datasets, and for example, after deploying epoch cut and eliminating obscure data (20% of data) by automatic postpruning, it achieved 100% accuracy for the rest 80% Iris data.

Evolving Fuzzy Clustering Approach: An Epoch Clustering That Enables Heuristic Postpruning

In this paper, the existence and uniqueness of the interface coupling (IC) of time and spatial (TS) arbitrary-order fractional (AOF) nonlinear hyperbolic scalar conservation laws (NHSCL) are investigated. The technique of arbitrary fractional characteristic method (AFCM) is used to accomplish this task. We apply Jumarie’s modification of Riemann–Liouville and Liouville–Caputo’s definition to extend some formulae to the arbitrary-order fractional calculus. Then these formulae are utilized to prove the main theorem. In this process, we develop an analytic method, which gives us the ability to find the solution of IC AOF NHSCL. The feature of this method is that it enables us to verify that the obtained solution satisfies the fractional partial differential equation (FPDE), and the solution is unique. Furthermore, a few examples illustrate the implementation of this technique in the application section.

/pdf/the-analytical-interface-coupling-of-arbitrary-order-66g5xauxfl.pdf

Ali Seyed Shirkhorshidi

Papers

Time-series clustering - A decade review

A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data

Big Data Clustering: A Review

Evolving Fuzzy Clustering Approach: An Epoch Clustering That Enables Heuristic Postpruning

The analytical interface coupling of arbitrary-order fractional nonlinear hyperbolic scalar conservation laws