Prediction of Missing Associations from Information System Using Intelligent Techniques

Prediction of Missing Associations from Information System Using Intelligent Techniques

Debi Prasanna Acharjya (VIT University, India) and V. Santhi (VIT University, India)
DOI: 10.4018/978-1-4666-8505-5.ch009
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Prediction of missing associations can be viewed as one of the most fundamental problems in the machine learning. The main objective of prediction of missing associations is to determine decisions for the missing associations. In real world problems, prediction of missing associations is must because absence of associations in the attribute values may have information to predict the decision for entrepreneurs. Based on decision theory, in the past many mathematical models such as naïve Bayes structure, human composed network structure, Bayesian network modeling etc. were developed. However, these theories have certain limitations. In order to overcome the limitations, rough computing is hybridized with Bayesian classification. This chapter discusses various techniques for predicting missing associations to obtain meaningful decision from information system. A real life example is provided to show the viability of the proposed research.
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Huge repository of data is accumulated across various domains at the present age of Internet. It is due to the wide spread of distributed computing which involves dispersion of data geographically. In addition, these data are neither crisp nor deterministic due to presence of uncertainty and vagueness. Obtaining meaningful information by analyzing these data is a great challenge for humans. Therefore, it is very difficult to extract expert knowledge from the universal dataset without any automated techniques. Also, there is much information hidden in these data. Most of our traditional tools for machine learning and knowledge extraction are crisp, deterministic and precise in character. So, it is essential for a new generation of computational theories and tools to assist human in extracting knowledge from the rapidly growing digital data. Knowledge discovery in databases (KDD) is the field that has evolved into an important and active area of research because of theoretical challenges associated with the problem of discovering intelligent solutions for huge data. Knowledge discovery and data mining is the rapidly growing interdisciplinary field which merges database management, statistics, computational intelligence and related areas. The basic aim of all these is knowledge extraction from voluminous data.

The processes of knowledge discovery in databases and machine learning appear deceptively simple when viewed from the perspective of terminological definition (Fayaad, 1996). The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data is known as knowledge discovery in databases. It consists of several stages such as data selection, cleaning of data, enrichment of data, coding, data mining and reporting. The different stages are shown in the following Figure 1. In addition, closely related process of information retrieval is defined by Rocha (2001) as “the methods and processes for searching relevant information out of information systems that contain extremely large numbers of documents”. However in execution, these processes are not simple at all, especially when executed to satisfy specific personal or organizational knowledge management requirements. It is also observed that, usefulness of an individual data element or pattern of data elements change dramatically from individual to individual, organization to organization, or task to task. It is because of the acquisition of knowledge and reasoning that involve in vagueness and incompleteness. In addition, knowledge extraction or description of data patterns generally understandable is also highly problematic. Therefore, there is much need for dealing with the incomplete and vague information in classification, concept formulation, and data analysis.

Figure 1.

The KDD Process


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