Analyzing Customer Behavior Using Online Analytical Mining (OLAM)

2015 ◽

pp. 98-118

Author(s):

Thanachart Ritbumroong

Keyword(s):

Data Mining ◽

Behavior Analysis ◽

Customer Behavior ◽

Future Research ◽

Technical Performance ◽

Data Mining Algorithms ◽

Integration Data ◽

Mining Algorithms ◽

Provided Examples

Online Analytical Mining (OLAM) is an architecture integrating data mining into OLAP. With this integration, data mining algorithms can be performed with OLAP abilities. OLAM enables users to choose a particular portion of data and analyze them with data mining models. Previous studies have provided examples of OLAM applications with the motivation to improve technical performance. This chapter reviews the capabilities of OLAM and discusses the well-known concept encompassing the analysis of customer behavior. The underlying motivation of this chapter is to present the opportunities for the development of OLAM to support the customer behavior analysis. Three main directions of the advancement in OLAM are proposed for future research.

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Identifying Decision Structures Underlying Activity Patterns: An Exploration of Data Mining Algorithms

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1718-01 ◽

2000 ◽

Vol 1718 (1) ◽

pp. 1-9 ◽

Cited By ~ 39

Author(s):

Geert Wets ◽

Koen Vanhoof ◽

Theo Arentze ◽

Harry Timmermans

Keyword(s):

Data Mining ◽

Decision Tree ◽

Logit Model ◽

Goodness Of Fit ◽

Travel Demand ◽

Activity Patterns ◽

Future Research ◽

Data Set ◽

Data Mining Algorithms ◽

Mining Algorithms

The utility-maximizing framework—in particular, the logit model—is the dominantly used framework in transportation demand modeling. Computational process modeling has been introduced as an alternative approach to deal with the complexity of activity-based models of travel demand. Current rule-based systems, however, lack a methodology to derive rules from data. The relevance and performance of data-mining algorithms that potentially can provide the required methodology are explored. In particular, the C4 algorithm is applied to derive a decision tree for transport mode choice in the context of activity scheduling from a large activity diary data set. The algorithm is compared with both an alternative method of inducing decision trees (CHAID) and a logit model on the basis of goodness-of-fit on the same data set. The ratio of correctly predicted cases of a holdout sample is almost identical for the three methods. This suggests that for data sets of comparable complexity, the accuracy of predictions does not provide grounds for either rejecting or choosing the C4 method. However, the method may have advantages related to robustness. Future research is required to determine the ability of decision tree-based models in predicting behavioral change.

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Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach

Journal of Solar Energy Engineering ◽

10.1115/1.3142727 ◽

2009 ◽

Vol 131 (3) ◽

Cited By ~ 65

Author(s):

Haiyang Zheng ◽

Andrew Kusiak

Keyword(s):

Data Mining ◽

Time Series ◽

Wind Farm ◽

Multivariate Time Series ◽

Future Research ◽

Support Vector ◽

Time Series Models ◽

Ramp Rate ◽

Data Mining Algorithms ◽

Mining Algorithms

In this paper, multivariate time series models were built to predict the power ramp rates of a wind farm. The power changes were predicted at 10 min intervals. Multivariate time series models were built with data-mining algorithms. Five different data-mining algorithms were tested using data collected at a wind farm. The support vector machine regression algorithm performed best out of the five algorithms studied in this research. It provided predictions of the power ramp rate for a time horizon of 10–60 min. The boosting tree algorithm selects parameters for enhancement of the prediction accuracy of the power ramp rate. The data used in this research originated at a wind farm of 100 turbines. The test results of multivariate time series models were presented in this paper. Suggestions for future research were provided.

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Multi-Relational Data Mining A Comprehensive Survey

Improving Knowledge Discovery through the Integration of Data Mining Techniques - Advances in Data Mining and Database Management ◽

10.4018/978-1-4666-8513-0.ch003 ◽

2015 ◽

pp. 32-53

Author(s):

Ali H. Gazala ◽

Waseem Ahmad

Keyword(s):

Data Mining ◽

Search Space ◽

Data Representation ◽

Research Area ◽

Relational Data ◽

Future Research ◽

Relational Data Mining ◽

Data Mining Algorithms ◽

Table Data ◽

Mining Algorithms

Multi-Relational Data Mining or MRDM is a growing research area focuses on discovering hidden patterns and useful knowledge from relational databases. While the vast majority of data mining algorithms and techniques look for patterns in a flat single-table data representation, the sub-domain of MRDM looks for patterns that involve multiple tables (relations) from a relational database. This sub-domain has received an increased research attention during the last two decades due to the wide range of possible applications. As a result of that growing attention, many successful multi-relational data mining algorithms and techniques were presented. This chapter presents a comprehensive review about multi-relational data mining. It discusses the different approaches researchers have followed to explore the relational search space while highlighting some of the most significant challenges facing researchers working in this sub-domain. The chapter also describes number of MRDM systems that have been developed during the last few years and discusses some future research directions in this sub-domain.

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Directions for Future Research on the Automatic Design of Data Mining Algorithms

Natural Computing Series - Automating the Design of Data Mining Algorithms ◽

10.1007/978-3-642-02541-9_7 ◽

2009 ◽

pp. 177-184 ◽

Cited By ~ 1

Author(s):

Gisele L. Pappa ◽

Alex A. Freitas

Keyword(s):

Data Mining ◽

Future Research ◽

Automatic Design ◽

Data Mining Algorithms ◽

Mining Algorithms

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Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

Current Drug Safety ◽

10.2174/1574886313666181026100000 ◽

2019 ◽

Vol 14 (1) ◽

pp. 21-26 ◽

Cited By ~ 2

Author(s):

Viswam Subeesh ◽

Eswaran Maheswari ◽

Hemendra Singh ◽

Thomas Elsa Beulah ◽

Ann Mary Swaroop

Keyword(s):

Data Mining ◽

Adverse Event ◽

Adverse Events ◽

Reporting System ◽

Adverse Event Reporting System ◽

Adverse Event Reporting ◽

Disproportionality Analysis ◽

Positive Signal ◽

Data Mining Algorithms ◽

Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

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