There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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 - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

CA : A Cancer Journal for Clinicians

Electronic commerce (e-commerce) has grown rapidly over the past years Products, services and information of different types are offered daily for many Internet users Finding out an appropriate strategy to offer a product to each customer in a personalized fashion is the goal of a recommender system This association between items is a task that falls under the umbrella of data mining, more specifically the area of association rule mining, or simply, association rules This paper investigates the use of evolutionary algorithms and artificial immune systems to build associations of items in real-world e-commerce databases The performances of the evolutionary and immune algorithms for rule mining are compared to each other A discussion is made in terms of a group of interest measures of associations and computational time necessary to learn the rules

Bioinspired Algorithms Applied to Association Rule Mining in Electronic Commerce Databases

Recent Advances in Gene Expression Data Clustering: A Case Study with Comparative Results.

Artificial neural networks (ANN) are rising as a promising heuristic approach to solve combinatorial optimization problems. Those based on self-organizing maps have presented themselves as the most promising ones because their results are very competitive, despite their apparent drawback of being totally unsupervised. This paper presents a two-phase algorithm based on artificial neural networks to solve three routing problems: traveling salesman, multiple traveling salesmen and capacitated vehicle routing. Several tests for the three problems are conducted on standard data and the results are compared with other proposals based on ANN from the literature, suggesting that the proposed approach is competitive.

A Clustering Approach Based on Artificial Neural Networks to Solve Routing Problems

Bacterial colonies perform a cooperative and distributed exploration of the environmental resources by using their quorum-sensing mechanisms. This paper describes how bacterial colony networks and their skills to explore resources can be used as tools for mining association rules in static and stream data. A new algorithm is designed to maintain diverse solutions to the problems at hand, and its performance is compared to that of other well-known bacteria, genetic, and immune-inspired algorithms: Bacterial Foraging Optimization (BFO), a Genetic Algorithm (GA), and the Clonal Selection Algorithm (CLONALG). Taking into account the superior performance of our approach in static data, we applied the algorithms to dynamic environments by converting static into flow data via a stream data model named sliding-window. We also provide some notes on the running time of the proposed algorithm using different hardware and software architectures.

/pdf/bacterial-colony-algorithms-for-association-rule-mining-in-2ehc5nttsa.pdf

Bacterial Colony Algorithms for Association Rule Mining in Static and Stream Data

The amount of data generated in different knowledge areas has made necessary the use of data mining tools capable of automatically analyzing and extracting knowledge from datasets. Clustering is one of the most important tasks in data mining and can be defined as the process of partitioning objects into groups or clusters, such that objects in the same group are more similar to one another than to objects belonging to other groups. In this context, this paper aims to propose a new encoding scheme to Copt Bees, a bee-inspired algorithm to solve data clustering problems. In this new encoding, each bee represents a prototype for the clusters. The algorithm was run for different datasets and the results obtained showed high quality clusters and diversity of solutions, whilst a suitable number of clusters was automatically determined.

http://www.researchgate.net/profile/Leandro_De_Castro2/publication/261295824_A_New_Encoding_Scheme_for_a_Bee-Inspired_Optimal_Data_Clustering_Algorithm/links/00b7d533d127e0ff13000000.pdf

Leandro Nunes de Castro

Papers

Bioinspired Algorithms Applied to Association Rule Mining in Electronic Commerce Databases

Recent Advances in Gene Expression Data Clustering: A Case Study with Comparative Results.

A Clustering Approach Based on Artificial Neural Networks to Solve Routing Problems

Bacterial Colony Algorithms for Association Rule Mining in Static and Stream Data

A New Encoding Scheme for a Bee-Inspired Optimal Data Clustering Algorithm