INTRODUCTION TO QUANTITATIVE RESEARCHES IN MANAGEMENT (CASE STUDY: APPLICATIONS OF DATA-MINING IN MANAGEMENT)

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NOORI BROOJERDI PEYMAN; ESGANDARI VAHAB

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Journal: QUANTITATIVE RESEARCHES IN MANAGEMENT Year:2011 | Volume:1 | Issue:3 Page(s): 1-13

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Title

INTRODUCTION TO QUANTITATIVE RESEARCHES IN MANAGEMENT (CASE STUDY: APPLICATIONS OF DATA-MINING IN MANAGEMENT)

Author(s)

NOORI BROOJERDI PEYMAN | ESGANDARI VAHAB | Issue Writer Certificate

Keywords

DATA-MININGQ2

CREDIT SCORINGQ3

DECISION TREESQ2

Abstract

DATA-MINING can be considered a recently developed methodology and technology, coming into prominence only in 1994. It aims to identify valid, novel, potentially useful and understandable correlations and patterns in data. DATA-MINING can also be considered as a process and technology to detect the previously unknown in order to gain competitive advantage. In data- mining, there is a strong emphasis on combing through copious data sets to sniff out patterns that are too subtle or complex for humans to detect. DATA-MINING techniques also include non-traditional methods developed in the areas of artificial intelligence and machine learning. The two most important models of these are neural networks and DECISION TREES. Neural networks are useful for recognizing patterns in the data, especially when the form of relationships between the target and input variables is unknown and/or complex. They are modeled after the human brain, which can be perceived as a highly connected network of neurons or nodes. Each node in a layer of nodes receives inputs from at least one node in a previous layer and combines the inputs and generates an output to at least one node in the next layer. Generally, the input variables comprise the input layer and the target variable comprises the output layer. Between the input and output layers, there may be one or more hidden layers of nodes. The objective of DECISION TREES is prediction and/or classification by dividing observations into mutually exclusive and collectively exhaustive subgroups. This process of division or splitting usually continues until either no further splitting can produce statistically significant differences in the target variable in the new subgroups or the subgroups are too small for any further meaningful division. The subgroups and sub-subgroups are usually referred to as nodes. The end product can be graphically represented by a tree-like structure. This study goes through the definition of Data Mining and its Techniques and gives a case study about its applications in CREDIT SCORING.

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APA: Copy

NOORI BROOJERDI, PEYMAN, & ESGANDARI, VAHAB. (2011). INTRODUCTION TO QUANTITATIVE RESEARCHES IN MANAGEMENT (CASE STUDY: APPLICATIONS OF DATA-MINING IN MANAGEMENT). QUANTITATIVE RESEARCHES IN MANAGEMENT, 1(3), 1-13. SID. https://sid.ir/paper/191943/en

Vancouver: Copy

NOORI BROOJERDI PEYMAN, ESGANDARI VAHAB. INTRODUCTION TO QUANTITATIVE RESEARCHES IN MANAGEMENT (CASE STUDY: APPLICATIONS OF DATA-MINING IN MANAGEMENT). QUANTITATIVE RESEARCHES IN MANAGEMENT[Internet]. 2011;1(3):1-13. Available from: https://sid.ir/paper/191943/en

IEEE: Copy

PEYMAN NOORI BROOJERDI, and VAHAB ESGANDARI, “INTRODUCTION TO QUANTITATIVE RESEARCHES IN MANAGEMENT (CASE STUDY: APPLICATIONS OF DATA-MINING IN MANAGEMENT),” QUANTITATIVE RESEARCHES IN MANAGEMENT, vol. 1, no. 3, pp. 1–13, 2011, [Online]. Available: https://sid.ir/paper/191943/en

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