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

Data Mining Practical Machine Learning Tools and Techniques

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

/pdf/survey-of-clustering-algorithms-6zezqhbjyo.pdf

Survey of clustering algorithms

http://masou-keshtkar.persiangig.com/document/sdarticle.pdf

GSA: A Gravitational Search Algorithm

A New Symmetry Based Genetic Clustering Technique for Automatic Evolution of Clusters

Ujjwal Maulik Sanghamitra Bandyopadhyay Sajal K. Das 16.

Medical Imaging and Diagnosis Using Genetic Algorithms.

Reduction of dimensionality has emerged as a routine process in modelling complex biological systems. A large number of feature selection techniques have been reported in the literature to improve model performance in terms of accuracy and speed. In the present article an unsupervised feature selection technique is proposed, using maximum information compression index as the dissimilarity measure and the well-known density-based cluster identification technique DBSCAN for identifying the largest natural group of dissimilar features. The algorithm is fast and less sensitive to the user-supplied parameters. Moreover, the method automatically determines the required number of features and identifies them. We used the proposed method for reducing dimensionality of a number of benchmark data sets of varying sizes. Its performance was also extensively compared with some other well-known feature selection methods.

Feature selection using feature dissimilarity measure and density-based clustering: Application to biological data

An important goal in microarray data analysis is to identify sets of genes with similar expression profiles. Clustering algorithms have been applied on microarray data either to group the genes across experimental conditions/samples [396, 44, 302, 355, 448] or group the samples across the genes [317, 339, 400, 384]. Clustering techniques, which aim to find the clusters of genes over all experimental conditions, may fail to discover the genes having similar expression patterns over a subset of conditions. Similarly, a clustering algorithm that groups conditions/samples across all the genes may not capture the group of samples having similar expression values for a subset of genes.

Multiobjective Biclustering in Microarray Gene Expression Data

Brain–Computer Interfacing helps in creation of a communication pathway between brain and external device such that the biological modality of performing the task could be bypassed. This necessitates fast and reliable decoding of brain signals which mandate feature selection to play a crucial role. The literature discloses the improvement in performance of left/right motor imagery signal classification with many-objective feature selection where several classification performance metrics have been maximized for obtaining a good quality feature set. This work analyses the classification performance by varying the feature dimension and number of objectives. A recent many-objective optimization coupled with objective reduction algorithm viz. \(\alpha \)-DEMO has been used for modeling the feature selection as an optimization problem with six objectives. The results obtained in this work have been statistically validated by Friedman Test.

Sanghamitra Bandyopadhyay

Papers

A New Symmetry Based Genetic Clustering Technique for Automatic Evolution of Clusters

Medical Imaging and Diagnosis Using Genetic Algorithms.

Feature selection using feature dissimilarity measure and density-based clustering: Application to biological data

Multiobjective Biclustering in Microarray Gene Expression Data

Exploration of Many-Objective Feature Selection for Recognition of Motor Imagery Tasks