Data Mining - Concepts and Techniques.

A Study of Thinking

Graph mining is an important research area within the domain of data mining The field of study concentrates on the identification of frequent subgraphs within graph data sets The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective This paper presents a survey of current research in the field of frequent subgraph mining and proposes solutions to address the main research issues

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A survey of frequent subgraph mining algorithms

With the cost of consuming resources increasing (both economically and ecologically), homeowners need to find ways to curb consumption. The Almanac of Minutely Power dataset Version 2 (AMPds2) has been released to help computational sustainability researchers, power and energy engineers, building scientists and technologists, utility companies, and eco-feedback researchers test their models, systems, algorithms, or prototypes on real house data. In the vast majority of cases, real-world datasets lead to more accurate models and algorithms. AMPds2 is the first dataset to capture all three main types of consumption (electricity, water, and natural gas) over a long period of time (2 years) and provide 11 measurement characteristics for electricity. No other such datasets from Canada exist. Each meter has 730 days of captured data. We also include environmental and utility billing data for cost analysis. AMPds2 data has been pre-cleaned to provide for consistent and comparable accuracy results amongst different researchers and machine learning algorithms.

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Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014

Knowledge Discovery and Data mining (KDD) is dedicated to exploring meaningful information from a large volume of data. "Knowledge Discovery and Data Mining: Challenges and Realities" is the most comprehensive reference publication for researchers and real-world data mining practitioners to advance knowledge discovery from low-quality data. This premier reference source presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying, low-quality data. International experts in the field of data mining have contributed all-inclusive chapters focusing on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing.

Knowledge Discovery and Data Mining: Challenges and Realities

Tree model guided candidate generation for mining frequent subtrees from XML

Tree mining has recently attracted a lot of interest in areas such as Bioinformatics, XML mining, Web mining, etc. We are mainly concerned with mining frequent induced and embedded subtrees. While more interesting patterns can be obtained when mining embedded subtrees, unfortunately mining such embedding relationships can be very costly. In this paper, we propose an efficient approach to tackle the complexity of mining embedded subtrees by utilizing a novel Embedding List representation, Tree Model Guided enumeration, and introducing the Level of Embedding constraint. Thus, when it is too costly to mine all frequent embedded subtrees, one can decrease the level of embedding constraint gradually up to 1, from which all the obtained frequent subtrees are induced subtrees. Our experiments with both synthetic and real datasets against two known algorithms for mining induced and embedded subtrees, FREQT and TreeMiner, demonstrate the effectiveness and the efficiency of the technique.

IMB3-Miner: mining induced/embedded subtrees by constraining the level of embedding

Assessing rules with interestingness measures is the pillar of successful application of association rules discovery. However, association rules discovered are normally large in number, some of which are not considered as interesting or significant for the application at hand. In this paper, we present a systematic approach to ascertain the discovered rules, and provide a precise statistical approach supporting this framework. The proposed strategy combines data mining and statistical measurement techniques, including redundancy analysis, sampling and multivariate statistical analysis, to discard the non- significant rules. Moreover, we consider real world datasets which are characterized by the uniform and non-uniform data/items distribution with a mixture of measurement levels throughout the data/items. The proposed unified framework is applied on these datasets to demonstrate its effectiveness in discarding many of the redundant or non-significant rules, while still preserving the high accuracy of the rule set as a whole.

Interestingness measures for association rules based on statistical validity

Due to the inherent flexibilities in both structure and semantics, XML association rules mining faces few challenges, such as: a more complicated hierarchical data structure and ordered data context. Mining frequent patterns from XML documents can be recast as mining frequent tree structures from a database of XML documents. In this study, we model a database of XML documents as a database of rooted labeled ordered subtrees. In particular, we are mainly concerned with mining frequent induced and embedded ordered subtrees. Our main contributions are as follows. We describe our unique embedding list representation of the tree structure, which enables efficient implementation of our Tree Model Guided (TMG) candidate generation. TMG is an optimal, nonredundant enumeration strategy that enumerates all the valid candidates that conform to the structural aspects of the data. We show through a mathematical model and experiments that TMG has better complexity compared to the commonly used join approach. In this article, we propose two algorithms, MB3-Miner and iMB3-Miner. MB3-Miner mines embedded subtrees. iMB3-Miner mines induced and/or embedded subtrees by using the maximum level of embedding constraint. Our experiments with both synthetic and real datasets against two well-known algorithms for mining induced and embedded subtrees, demonstrate the effectiveness and the efficiency of the proposed techniques.

Tree model guided candidate generation for mining frequent subtrees from XML documents

Mining of Data with Complex Structures:- Clarifies the type and nature of data with complex structure including sequences, trees and graphs- Provides a detailed background of the state-of-the-art of sequence mining, tree mining and graph mining.-Defines the essential aspects of the tree mining problem: subtree types, support definitions, constraints.- Outlines the implementation issues one needs to consider when developing tree mining algorithms (enumeration strategies, data structures, etc.)- Details the Tree Model Guided (TMG) approach for tree mining and provides the mathematical model for the worst case estimate of complexity of mining ordered induced and embedded subtrees.- Explains the mechanism of the TMG framework for mining ordered/unordered induced/embedded and distance-constrained embedded subtrees.- Provides a detailed comparison of the different tree mining approaches highlighting the characteristics and benefits of each approach.- Overviews the implications and potential applications of tree mining in general knowledge management related tasks, and uses Web, health and bioinformatics related applications as case studies.- Details the extension of the TMG framework for sequence mining- Provides an overview of the future research direction with respect to technical extensions and application areasThe primary audience is 3rd year, 4th year undergraduate students, Masters and PhD students and academics. The book can be used for both teaching and research. The secondary audiences are practitioners in industry, business, commerce, government and consortiums, alliances and partnerships to learn how to introduce and efficiently make use of the techniques for mining of data with complex structures into their applications. The scope of the book is both theoretical and practical and as such it will reach a broad market both within academia and industry. In addition, its subject matter is a rapidly emerging field that is critical for efficient analysis of knowledge stored in various domains.

Fedja Hadzic

Papers

Tree model guided candidate generation for mining frequent subtrees from XML

IMB3-Miner: mining induced/embedded subtrees by constraining the level of embedding

Interestingness measures for association rules based on statistical validity

Tree model guided candidate generation for mining frequent subtrees from XML documents

Mining of Data with Complex Structures