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Journal Article

Multidimensional and Data Mining Analysis for Property Investment Risk Analysis

TL;DR: A new technique to measure the uncertainty of the risk factors based on multidimensional data model and data mining techniques as deterministic approach is proposed.
Abstract: Property investment in the real estate industry has a high risk due to the uncertainty factors that will affect the decisions made and high cost. Analytic hierarchy process has existed for some time in which referred to an expert’s opinion to measure the uncertainty of the risk factors for the risk analysis. Therefore, different level of experts’ experiences will create different opinion and lead to the conflict among the experts in the field. The objective of this paper is to propose a new technique to measure the uncertainty of the risk factors based on multidimensional data model and data mining techniques as deterministic approach. The propose technique consist of a basic framework which includes four modules: user, technology, end-user access tools and applications. The property investment risk analysis defines as a micro level analysis as the features of the property will be considered in the analysis in this paper. Keywords—Uncertainty factors, data mining, multidimensional data model, risk analysis.
Citations
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Proceedings ArticleDOI
28 Jun 2010
TL;DR: This report gives a brief overview of the main concerns addressed by the authors at the first international workshop on Cooperative Knowledge Discovery & Data Mining (CKDD), held at WETICE 2010.
Abstract: This report gives a brief overview of the main concerns addressed by the authors at the first international workshop on Cooperative Knowledge Discovery & Data Mining (CKDD), held at WETICE 2010. A presentation of the main topics is given and then a summary of each paper accepted by the workshop is reported.

1 citations

Journal ArticleDOI
TL;DR: Evaluation of information from various domain experts to suggest the investor best investment option for his investment and decision tree technique is applied to this dataset to make investment decisions.
Abstract: Investment decision is a major issue for every individual. The spectrum of investment is extremely wide. Many investment options are available for the investor. People are not aware of best saving scheme for their investment. This paper deals with eliction of information from various domain experts to suggest the investor best investment option for his investment. The decision is based upon various parameters of the investor. A refined algorithm helps the investor to make an effective decision for his investment which suits their requirement. In this study data has been collected from 500 persons through questionnaires and interviews. Expert’s rules and feature reduction technique have been applied to this data set to convert it into an optimal dataset. Decision tree technique is applied to this dataset to make investment decisions. Finally, the comparison has been done between our approach and existing approach.

1 citations

Proceedings ArticleDOI
01 Jan 2016
TL;DR: The results demonstrated that tree models named eXtreme Gradient Boosting Linear, eXTreme Gradients Boosting Tree and Random Forest respectively have best performance among other techniques and spatial information such drive distance and duration to CBD increase the predictive model performance significantly.
Abstract: Predicting the market value of a residential property accurately without inspection by professional valuer could be beneficial for vary of organization and people. Building an Automated Valuation Model could be beneficial if it will be accurate adequately. This paper examined 47 machine learning models (linear and non-linear). These models are fitted on 1967 records of units from 19 suburbs of Sydney, Australia. The main aim of this paper is to compare the performance of these techniques using this data set and investigate the effect of spatial information on valuation accuracy. The results demonstrated that tree models named eXtreme Gradient Boosting Linear, eXtreme Gradient Boosting Tree and Random Forest respectively have best performance among other techniques and spatial information such drive distance and duration to CBD increase the predictive model performance significantly.

Cites background from "Multidimensional and Data Mining An..."

  • ...Some researches investigate the risk of investment in real estate industry that they mention the importance of accurate estimation of real estate value ([14], [15] and [16])....

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References
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Journal ArticleDOI
TL;DR: In this article, the authors present three major factors needed for managing opportunities: the ability of the project manager to develop a holistic view within the project, the organizational support and interest, and the ability to understand how other organizations affect the project objectives.

196 citations


"Multidimensional and Data Mining An..." refers background in this paper

  • ...that could cause negative consequences for the project and the organization [7]....

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Journal ArticleDOI
TL;DR: An extension of the OnLine Analytical Processing framework with causal explanation is described, offering the possibility to automatically generate explanations for exceptional cell values, suggesting improved decision-making by managers because the current tedious and error-prone manual analysis process is enhanced by automated problem identification and explanation generation.

82 citations


"Multidimensional and Data Mining An..." refers background in this paper

  • ...A research on how to design, build and implement intelligent decision making support system (i-DMSS) from a more structured and software engineering/systems engineering perspective are still missing in 1980 – 2004 period [13]....

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Proceedings ArticleDOI
23 Jan 2008
TL;DR: The relation between Knowledge and Data Mining, and Knowledge Discovery in Database (KDD) process are presented and data mining theory, Data mining tasks, Data Mining technology and data Mining challenges are proposed.
Abstract: Knowledge discovery and data mining have become areas of growing significance because of the recent increasing demand for KDD techniques, including those used in machine learning, databases, statistics, knowledge acquisition, data visualization, and high performance computing. Knowledge discovery and data mining can be extremely beneficial for the field of Artificial Intelligence in many areas, such as industry, commerce, government, education and so on. The relation between Knowledge and Data Mining, and Knowledge Discovery in Database (KDD) process are presented in the paper. Data mining theory, Data mining tasks, Data Mining technology and Data Mining challenges are also proposed. This is an belief abstract for an invited talk at the workshop.

69 citations


"Multidimensional and Data Mining An..." refers background in this paper

  • ...Data mining allows users to analyse data from different dimensions or angles, categorize it, and summarize the relationships identified [18]-[22]....

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Book ChapterDOI
01 Jan 2006
TL;DR: This chapter provides an overview of challenges for the future development of decision support technologies and their integration in intelligent decision-making support systems and an advanced Web-based decision making framework is proposed.
Abstract: Arguing that contemporary decision making needs to be tackled through a holistic perspective, in that the conceptual, methodological and application-oriented aspects of the problem have to be simultaneously taken into account, this chapter provides an overview of challenges for the future development of decision support technologies and their integration in intelligent decision-making support systems. Based on this discussion, and aiming at providing decision makers around the world with applications of enhanced performance, while, at the same time, addressing their communication and collaboration needs in an efficient and effective way, an advanced Web-based decision making framework is proposed.

29 citations


"Multidimensional and Data Mining An..." refers background in this paper

  • ...data from several online transaction processing (OLTP) databases [17]....

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Proceedings ArticleDOI
31 Oct 2008
TL;DR: The new understanding for data mining, domain-oriented data-driven data mining (3DM), will be introduced, the relationship of 3DM and GrC, and granular computing based data mining in the views of rough set and fuzzy set will be discussed.
Abstract: Usually, data mining is considered as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. In our data-driven data mining model, knowledge is originally existed in data, but just not understandable for human. Data mining is taken as a process of transforming knowledge from data format into some other human understandable format like rule, formula, theorem, etc. In order to keep the knowledge unchanged in a data mining process, the knowledge properties should be kept unchanged during a knowledge transformation process. Many real world data mining tasks are highly constraint-based and domain-oriented. Thus, domain prior knowledge should also be a knowledge source for data mining. The control of a user to a data mining process could also be taken as a kind of dynamic input of the data mining process. Thus, a data mining process is not only mining knowledge from data, but also from human. This is the key idea of Domain- oriented Data-driven Data Mining (3DM). In the view of granular computing (GrC), a data mining process can be considered as the transformation of knowledge in different granularities. Original data is a representation of knowledge in the finest granularity. It is not understandable for human. However, human is sensitive to knowledge in coarser granularities. So, a data mining process could be considered to be a transformation of knowledge from a finer granularity space to a coarser granularity space. The understanding for data mining of3DM and GrC is consistent to each other. Rough set and fuzzy set are two important computing paradigms of GrC. They are both generalizations of classical set theory for modeling vagueness and uncertainty. Although both of them can be used to address vagueness, they are not rivals. In some real problems, they are even complementary to each other. In this plenary talk, the new understanding for data mining, domain-oriented data-driven data mining (3DM), will be introduced. The relationship of 3DM and GrC, and granular computing based data mining in the views of rough set and fuzzy set will be discussed.

25 citations


"Multidimensional and Data Mining An..." refers background in this paper

  • ...return required to attract direct investment into property [9]....

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  • ...The ultimate aim of any investor is to maximize his returns and minimize the risk [9]....

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