Imprecise Data and the Data Mining Process

Imprecise Data and the Data Mining Process

Marvin L. Brown (Grambling State University, USA) and John F. Kros (East Carolina University, USA)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-60566-010-3.ch155
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Abstract

Missing or inconsistent data has been a pervasive problem in data analysis since the origin of data collection. The management of missing data in organizations has recently been addressed as more firms implement large-scale enterprise resource planning systems (see Vosburg & Kumar, 2001; Xu et al., 2002). The issue of missing data becomes an even more pervasive dilemma in the knowledge discovery process, in that as more data is collected, the higher the likelihood of missing data becomes. The objective of this research is to discuss imprecise data and the data mining process. The article begins with a background analysis, including a brief review of both seminal and current literature. The main thrust of the chapter focuses on reasons for data inconsistency along with definitions of various types of missing data. Future trends followed by concluding remarks complete the chapter.
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Reasons For Data Inconsistency

Data inconsistency may arise for a number of reasons, including:

  • Procedural Factors

  • Refusal of Response

  • Inapplicable Responses

These three reasons tend to cover the largest areas of missing data in the data mining process.

Procedural Factors

Data entry errors are common and their impact on the knowledge discovery process and data mining can generate serious problems. Inaccurate classifications, erroneous estimates, predictions, and invalid pattern recognition may also take place. In situations where databases are being refreshed with new data, blank responses from questionnaires further complicate the data mining process. If a large number of similar respondents fail to complete similar questions, the deletion or misclassification of these observations can take the researcher down the wrong path of investigation or lead to inaccurate decision-making by end users.

Refusal of Response

Some respondents may find certain survey questions offensive or they may be personally sensitive to certain questions. For example, some respondents may have no opinion regarding certain questions such as political or religious affiliation. In addition, questions that refer to one’s education level, income, age or weight may be deemed too private for some respondents to answer.

Furthermore, respondents may simply have insufficient knowledge to accurately answer particular questions. Students or inexperienced individuals may have insufficient knowledge to answer certain questions (such as salaries in various regions of the country, retirement options, insurance choices, etc).

Inapplicable Responses

Sometimes questions are left blank simply because the questions apply to a more general population rather than to an individual respondent. If a subset of questions on a questionnaire does not apply to the individual respondent, data may be missing for a particular expected group within a data set. For example, adults who have never been married or who are widowed or divorced are likely to not answer a question regarding years of marriage.

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