Knowledge Discovery in Databases: An Information Retrieval Perspective

The current trend of increasing capabilities in data generation and collection has resulted in an urgent need for data mining applications, also called knowledge discovery in databases. This paper identifies and examines the issues involved in extracting useful grains of knowledge from large amounts of data. It describes a framework to categorise data mining systems. The author also gives an overview of the issues pertaining to data pre processing, as well as various information gathering methodologies and techniques. The paper covers some popular tools such as classification, clustering, and generalisation. A summary of statistical and machine learning techniques used currently is also provided

Market Basket Analysis in the Financial Sector – A Customer Centric Approach

Organizations often struggle with their efforts to implement data mining projects successfully. This is often due to the fact that they are influenced by success stories of others that glamorize the outcome of successful initiatives, while understating the persistent rigour and diligence required. Although process models exist for the knowledge discovery process their focus is often on outlining the activities that must be done and not on describing how they should be done. While there is some research in addressing how to carry out the various tasks in the phases, the data preparation phase is thought to be the most challenging and is often described as an art rather than a science. In this study we apply a multi-phased integrated knowledge discovery and data mining process model (IKDDM) to a data set from the financial sector and a present a new approach to data preparation for Sequential Patterns (SP) that facilitated the identification of customer focused patterns rather than products focussed patterns in the modelling phase

Encapsulation of Soft Computing Approaches within Itemset Mining a A Survey

Data Mining discovers patterns and trends by extracting knowledge from large databases. Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc. aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide efficient search algorithms for selecting a model, from mixed media data. Data mining refers to information extraction while soft computing is used for information processing. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. This survey paper explores the usage of soft computing approaches in itemset utility mining

The need to use data mining techniques in E-Business

Abstract. The number of Internet users rose from 400 million in 2000 to just over 2 billion in early 2011. This means that approximately one third of the world's population uses the internet. Taking  these conditions into consideration, we can say that businesses have changed their way. Many companies that, over the last century could not even dream that could have a certain volume of activity or they could face competition with industry giants, have succeeded in giving to enjoy great success.  For example: Amazon.com, founded in 1995, had in 1999 a turnover of at least 13 times higher than other prestigious names in the U.S., such as Barnes & Noble and Borders Books & Music. E-business is the key to make life easier for the people. Knowledge of e-business environment is essential for doing business in this century. More must be understood and new technologies applied to extract knowledge from data