Classification is a fundamental data mining technique used to predict group membership for data instances. Conventional classifiers such as decision tree, Bayesian classifier and k-nearest neighbor suffer from serious drawbacks in the presence of noise and uncertainty in data sets. Also, they are unable to handle multiple instances with overlapping attributes that belong to different classes. These limitations are addressed by fuzzy classification. Fuzzy classifiers provide a smooth classification boundary as compared to traditional methods. Further, fuzzy rule-based classifiers are easy to interpret, capable of optimizing more than one objective simultaneously and popular for real-life applications such as face and voice recognition, and handwriting verification. The paper, “Multi-Objective Genetic Algorithms for Fuzzy Classification Rule Mining: A Survey”, by Harihar Kalia, Satchidananda Dehuri and Ashish Ghosh presents an in-depth survey of classification rule mining using multi-objective Genetic Algorithms (GAs). The authors conclude that qualitative measures of fuzzy rule-based system, learning and tuning of membership functions, and concavity of the problem require further intensive research for advancement of this hybrid approach.

Most businesses gather enormous amount of data from specific geographical locations that help them improve business performance and increase profitability. For example, a shorter distance between a supermarket and residential colonies definitely influences the customers visiting the supermarket compared to another that is located far away. Many critical business decisions involve a location component. Location intelligence is a technological method of gathering and processing data such as economics, demographics, physical geography and other related data pertaining to location in order to better understand customers, markets and services to be offered by the business organization. Location intelligence helps detect patterns, evaluate risks and opportunities which may not be possible in a conventional spreadsheet analysis.

It offers a range of benefits which ultimately translate to increased revenue, reduced costs and improved efficiency for the business. The paper, “Location Intelligence Mashup Using Open Source Software and Google Maps API”, by K V N Rajesh describes an approach to implement location intelligence by using various open source web technologies, Google Maps Application Program Interface (API) and MySQL database. This approach displays the city-wise sales revenue for a fictional retail chain. It provides a good example of how diverse open source software and APIs can be intelligently fit together to create a smart working application that can deliver business value.

Cybercrimes severely affect the business and the society. There are several types of cybercrimes which range from hacking into a computer network to phishing that gives a false sense of security, prompting the users to divulge sensitive information. There is no panacea to these global phenomena. However, understanding the nature and effects of various cybercrimes is an important first step in comprehending the necessary security measures for preventing the crimes as well as minimizing the damaging consequences of the crimes. The paper, “Cybercrimes and the Nigerian Academic Institution Networks”, by Shafi’i Muhammad Abdulhamid, Chiroma Haruna and Adamu Abubakar, discusses different types of cybercrimes that are prevalent in Nigeria.

It presents a survey on the crimes that are carried out by the use of academic institution networks as the access point. The findings show that the Yahoo Boys attack is very popular even in the academia and that students are the most active participants in cybercrimes in the Nigerian institution networks. It is also found that it is possible to create a taxonomy of scams and scammers, and develop tools, measures, campaigns and laws that will deal with the menace of cybercrime.

Small and Medium-sized Enterprises (SMEs) are the driving forces for employment generation, economic growth of the nation and competitiveness in business. Indian auto ancillary SMEs are rapidly growing and India is becoming a hub for many global auto majors. Today in the globalization era, the use of Information Technology (IT) is very essential for a business. It enables the firm to integrate with their global and domestic customers. It improves process efficiency and enhances competitiveness. However, IT penetration into SMEs in India is lagging behind their global counterparts. The paper, “Determinants of Basic IT Adoption by Auto Ancillary SMEs in India”, by P Dharmalingam and G Kannabiran, presents a study of basic IT adoption in auto ancillary sector. The findings show that lack of financial capacity, lack of in-house IT manpower and small-scale operation are the major determinants which cause the low rate of adoption in India. At the same time, perceived benefits, perceived competitive pressure and awareness of changes in business environment are the motivating factors for such adoption.

-- A C Ojha
Consulting Editor