Integrating Economic Knowledge in Data Mining Algorithms

The assessment of knowledge derived from databases depends on many factors. Decision makers often need to convince others about the correctness and effectiveness of knowledge induced from data.The current data mining techniques do not contribute much to this process of persuasion.Part of this limitation can be removed by integrating knowledge from experts in the field, encoded in some accessible way, with knowledge derived form patterns stored in the database.In this paper we will in particular discuss methods for implementing monotonicity constraints in economic decision problems.This prior knowledge is combined with data mining algorithms based on decision trees and neural networks.The method is illustrated in a hedonic price model.knowledge;neural network;data mining;decision trees

Derivation of Monotone Decision Models from Non-Monotone Data

The objective of data mining is the extraction of knowledge from databases. In practice, one often encounters difficulties with models that are constructed purely by search, without incorporation of knowledge about the domain of application.In economic decision making such as credit loan approval or risk analysis, one often requires models that are monotone with respect to the decision variables involved.If the model is obtained by a blind search through the data, it does mostly not have this property even if the underlying database is monotone.In this paper, we present methods to enforce monotonicity of decision models.We propose measures to express the degree of monotonicity of the data and an algorithm to make data sets monotone.In addition, it is shown that monotone decision trees derived from cleaned data perform better compared to trees derived from raw data.decision models;knowledge;decision theory;operational research;data mining

Data Mining: How Popular Is It?

Data Mining is a process used in the industry, to facilitate decision making. As the name implies, large volumes of data is mined or sifted, to find useful information for decision making. With the advent of E-business, Data Mining has become more important to practitioners. The purpose of this paper is to find out the importance of Data Mining by looking at the different application areas that have used data mining for decision making

Using Rule Based Classifiers for the Predictive Analysis of Breast Cancer Recurrence

The Aim of this work is to assess the Effectiveness of Rule Based Classifiers to help an Oncology Doctor for prediction of Breast Cancer Recurrence ,286 Cancer patient data ,obtained from UCI Machine learning Repository ,are used to determine Recurrence Events for New patients .This dataset is processed with WEKA Data Mining Tool ,by applying Rule Based Classifiers(RIPPER,DT,DTNB) and Rule Set is generated .Further from Experimental Results, it has been found that DTNB is providing improved Accuracy compared to other two Classifiers .Based on the patients’ characteristics and the Rule set generated by DTNB ,New patients may be labeled as developing or not Recurrence Events,thus supporting an Oncology Doctor in making Decisions about disease in a shorter time. Keywords: Medical Data Mining, KDD, Breast Cancer Recurrence, Classification, Association, WEKA, Rule Based Classifier

Analysis for Detecting and Explaining Exceptions in Business Data

In this paper we describe the concepts of automatic analysis for the exceptional patterns which are hidden in a large set of business data. These exceptions are interesting to be investigated further for their causes and explanations, as they provide important decision support. The analysis process is driven by diagnostic drill-down operations following the equations of the information structure in which the data are organised. Using business intelligence, the analysis method can generate explanations supported by the data. The methodology was tested on a case study and was reflected in considering the practical aspects of its application procedure

Competence Based Management in Academics through Data Mining Approach

Competence based Management through Data mining approach helps academia to improve research and academic decision making through uncovering hidden trends and patterns that predicts using a combination of explicit knowledge base, sophisticated analytical skills and academic domain knowledge. The paper proposes a framework for effective educational process using Data Mining techniques to uncover the hidden trends and patterns and making accuracy based predictions through higher level of analytical sophistication in students counseling process. Keywords: Faculty; Faculty Assessment; Faculty; Competence Management; Data Mining; Patterns

Application based technical Approaches of data mining in Pharmaceuticals, and Research approaches in biomedical and Bioinformatics

In the past study shows that flow of direction in the field of pharmaceutical was quit slow and simplest and by the time the process of transformation of information was so complex and the it was out of the reach to the technology, new modern technology could not reach to catch the pharmaceutical field. Then the later on technology becomes the compulsorily part of business and its contributed into business progress and developments. But now a days its get technology enabled and smoothly and easily pharma industries managing their billings and inventories and developing new products and services and now its easy to maintain and merging the drugs detail like its cost ,and usage with the patients records prescribe by the doctors in the hospitals .and data collection methods have improved data manipulation techniques are yet to keep pace with them data mining called and refer with the specific term as pattern analysis on large data sets used like clustering, segmentation and classification for helping better manipulation of the data and hence it helps to the pharma firms and industries this paper describes the vital role of data Mining in the pharma industry and thus data mining improves the quality of decision making services in pharmaceutical fields. This paper also describe a brief overviews of tool kits of Data mining and its various Applications in the field of Biomedical research in terms of relational approaches of data minings with the Emphasis on propositionalisation and relational subgroup discovery, and which is quit helpful to prove to be effective for data analysis in biomedical and its applications and in Bioinformatics as well. DOI: 10.17762/ijritcc2321-8169.15038

Text Classification with Imperfect Hierarchical Structure Knowledge

Many real world classification problems involve classes organized in a hierarchical tree-like structure. However in many cases the hierarchical structure is ignored and each class is treated in isolation or in other words the class structure is flattened (Dumais and Chen, 2000). In this paper, we propose a new approach of incorporating hierarchical structure knowledge by cascading it as an additional feature for Child level classifier. We posit that our cascading model will outperform the baseline “flat” model. Our empirical experiment provides strong evidences supporting our proposal. Interestingly, even imperfect hierarchical structure knowledge would also improve classification performance

Mining the FX electronic inter-dealer market

This paper examines the feasibility of applying data mining techniques to testing market efficiency hypothesis using a high frequency, up to one thousand of a second, electronic brokerage data. Results suggest the existence of a pattern of negative autocorrelation in returns of DEM/USD over relative short lags (less than 40 seconds). However, this pattern is not feasible by two reasons: (1) the structure of autocorrelation pattern is inconsistent and changes too rapidly (2) the largest potential speculative profit is smaller than the regulated tick size. These results indicate that dealers have engaged in any potential profitable speculations based on past price information