Data mining as a tool for environmental scientists

Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous

On the role of pre and post-processing in environmental data mining

The quality of discovered knowledge is highly depending on data quality. Unfortunately real data use to contain noise, uncertainty, errors, redundancies or even irrelevant information. The more complex is the reality to be analyzed, the higher the risk of getting low quality data. Knowledge Discovery from Databases (KDD) offers a global framework to prepare data in the right form to perform correct analyses. On the other hand, the quality of decisions taken upon KDD results, depend not only on the quality of the results themselves, but on the capacity of the system to communicate those results in an understandable form. Environmental systems are particularly complex and environmental users particularly require clarity in their results. In this paper some details about how this can be achieved are provided. The role of the pre and post processing in the whole process of Knowledge Discovery in environmental systems is discussed

A COMPREHENSIVE GEOSPATIAL KNOWLEDGE DISCOVERY FRAMEWORK FOR SPATIAL ASSOCIATION RULE MINING

Continuous advances in modern data collection techniques help spatial scientists gain access to massive and high-resolution spatial and spatio-temporal data. Thus there is an urgent need to develop effective and efficient methods seeking to find unknown and useful information embedded in big-data datasets of unprecedentedly large size (e.g., millions of observations), high dimensionality (e.g., hundreds of variables), and complexity (e.g., heterogeneous data sources, space–time dynamics, multivariate connections, explicit and implicit spatial relations and interactions). Responding to this line of development, this research focuses on the utilization of the association rule (AR) mining technique for a geospatial knowledge discovery process. Prior attempts have sidestepped the complexity of the spatial dependence structure embedded in the studied phenomenon. Thus, adopting association rule mining in spatial analysis is rather problematic. Interestingly, a very similar predicament afflicts spatial regression analysis with a spatial weight matrix that would be assigned a priori, without validation on the specific domain of application. Besides, a dependable geospatial knowledge discovery process necessitates algorithms supporting automatic and robust but accurate procedures for the evaluation of mined results. Surprisingly, this has received little attention in the context of spatial association rule mining. To remedy the existing deficiencies mentioned above, the foremost goal for this research is to construct a comprehensive geospatial knowledge discovery framework using spatial association rule mining for the detection of spatial patterns embedded in geospatial databases and to demonstrate its application within the domain of crime analysis. It is the first attempt at delivering a complete geo-spatial knowledge discovery framework using spatial association rule mining

Data mining by means of generalized patterns

The thesis is mainly focused on the study and the application of pattern discovery algorithms that aggregate database knowledge to discover and exploit valuable correlations, hidden in the analyzed data, at different abstraction levels. The aim of the research effort described in this work is two-fold: the discovery of associations, in the form of generalized patterns, from large data collections and the inference of semantic models, i.e., taxonomies and ontologies, suitable for driving the mining proces

Pattern Mining and Sense-Making Support for Enhancing the User Experience

While data mining techniques such as frequent itemset and sequence mining are well established as powerful pattern discovery tools in domains from science, medicine to business, a detriment is the lack of support for interactive exploration of high numbers of patterns generated with diverse parameter settings and the relationships among the mined patterns. To enhance the user experience, real-time query turnaround times and improved support for interactive mining are desired. There is also an increasing interest in applying data mining solutions for mobile data. Patterns mined over mobile data may enable context-aware applications ranging from automating frequently repeated tasks to providing personalized recommendations. Overall, this dissertation addresses three problems that limit the utility of data mining, namely, (a.) lack of interactive exploration tools for mined patterns, (b.) insufficient support for mining localized patterns, and (c.) high computational mining requirements prohibiting mining of patterns on smaller compute units such as a smartphone. This dissertation develops interactive frameworks for the guided exploration of mined patterns and their relationships. Contributions include the PARAS pre- processing and indexing framework; enabling analysts to gain key insights into rule relationships in a parameter space view due to the compact storage of rules that enables query-time reconstruction of complete rulesets. Contributions also include the visual rule exploration framework FIRE that presents an interactive dual view of the parameter space and the rule space, that together enable enhanced sense-making of rule relationships. This dissertation also supports the online mining of localized association rules computed on data subsets by selectively deploying alternative execution strategies that leverage multidimensional itemset-based data partitioning index. Finally, we designed OLAPH, an on-device context-aware service that learns phone usage patterns over mobile context data such as app usage, location, call and SMS logs to provide device intelligence. Concepts introduced for modeling mobile data as sequences include compressing context logs to intervaled context events, adding generalized time features, and identifying meaningful sequences via filter expressions

A new approach of top-down induction of decision trees for knowledge discovery

Top-down induction of decision trees is the most popular technique for classification in the field of data mining and knowledge discovery. Quinlan developed the basic induction algorithm of decision trees, ID3 (1984), and extended to C4.5 (1993). There is a lot of research work for dealing with a single attribute decision-making node (so-called the first-order decision) of decision trees. Murphy and Pazzani (1991) addressed about multiple-attribute conditions at decision-making nodes. They show that higher order decision-making generates smaller decision trees and better accuracy. However, there always exist NP-complete combinations of multiple-attribute decision-makings.;We develop a new algorithm of second-order decision-tree inductions (SODI) for nominal attributes. The induction rules of first-order decision trees are combined by \u27AND\u27 logic only, but those of SODI consist of \u27AND\u27, \u27OR\u27, and \u27OTHERWISE\u27 logics. It generates more accurate results and smaller decision trees than any first-order decision tree inductions.;Quinlan used information gains via VC-dimension (Vapnik-Chevonenkis; Vapnik, 1995) for clustering the experimental values for each numerical attribute. However, many researchers have discovered the weakness of the use of VC-dim analysis. Bennett (1997) sophistically applies support vector machines (SVM) to decision tree induction. We suggest a heuristic algorithm (SVMM; SVM for Multi-category) that combines a TDIDT scheme with SVM. In this thesis it will be also addressed how to solve multiclass classification problems.;Our final goal for this thesis is IDSS (Induction of Decision Trees using SODI and SVMM). We will address how to combine SODI and SVMM for the construction of top-down induction of decision trees in order to minimize the generalized penalty cost