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The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

This paper introduces support envelopes---a new tool for analyzing association patterns---and illustrates some of their properties, applications, and possible extensions. Specifically, the support envelope for a transaction data set and a specified pair of positive integers (m,n) consists of the items and transactions that need to be searched to find any association pattern involving m or more transactions and n or more items. For any transaction data set with M transactions and N items, there is a unique lattice of at most M*N support envelopes that captures the structure of the association patterns in that data set. Because support envelopes are not encumbered by a support threshold, this support lattice provides a complete view of the association structure of the data set, including association patterns that have low support. Furthermore, the boundary of the support lattice---the support boundary---has at most min(M,N) envelopes and is especially interesting since it bounds the maximum sizes of potential association patterns---not only for frequent, closed, and maximal itemsets, but also for patterns, such as error-tolerant itemsets, that are more general. The association structure can be represented graphically as a two-dimensional scatter plot of the (m,n) values associated with the support envelopes of the data set, a feature that is useful in the exploratory analysis of association patterns. Finally, the algorithm to compute support envelopes is simple and computationally efficient, and it is straightforward to parallelize the process of finding all the support envelopes.

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Support envelopes: a technique for exploring the structure of association patterns

https://www.cse.msu.edu/~ptan/papers/neurocomp2011.pdf

A framework for joint community detection across multiple related networks

Weather forecasting is an important but challenging problem as one must contend with the inherent non-linearities and spatiotemporal autocorrelation present in the data. This paper presents a novel deep learning approach based on a coupled weighted graph convolutional LSTM (WGC-LSTM) to address these challenges. Specifically, our proposed approach uses an LSTM to capture the inherent temporal autocorrelation of the data and a graph convolution to model its spatial relationships. As the weather condition can be influenced by various spatial factors besides the distance between locations, e.g., topography, prevailing winds and jet streams, imposing a fixed graph structure based on the proximity between locations is insufficient to train a robust deep learning model. Instead, our proposed approach treats the adjacency matrix of the graph as a model parameter that can be learned from the training data. However, this introduces an additional O(|V|^2) parameters to be estimated, where V is the number of locations. With large graphs this may also lead to slower performance as well as susceptibility to overfitting. We propose a modified version of our approach that can address this difficulty by assuming that the adjacency matrix is either sparse or low rank. Experimental results using two real-world weather datasets show that WGC-LSTM outperforms all other baseline methods for the majority of the evaluated locations.

A Low Rank Weighted Graph Convolutional Approach to Weather Prediction

Ecology has a rich history of studying ecosystem dynamics across time and space that has been motivated by both practical management needs and the need to develop basic ideas about pattern and process in nature. In situations in which both spatial and temporal observations are available, similarities in temporal behavior among sites (i.e., synchrony) provide a means of understanding underlying processes that create patterns over space and time. We used pattern analysis algorithms and data spanning 22–25 yr from 601 lakes to ask three questions: What are the temporal patterns of lake water clarity at sub-continental scales? What are the spatial patterns (i.e., geography) of synchrony for lake water clarity? And, what are the drivers of spatial and temporal patterns in lake water clarity? We found that the synchrony of water clarity among lakes is not spatially structured at sub-continental scales. Our results also provide strong evidence that the drivers related to spatial patterns in water clarity are not related to the temporal patterns of water clarity. This analysis of long-term patterns of water clarity and possible drivers contributes to understanding of broad-scale spatial patterns in the geography of synchrony and complex relationships between spatial and temporal patterns across ecosystems.

Macroscale patterns of synchrony identify complex relationships among spatial and temporal ecosystem drivers

Medical predictive modeling is a challenging problem due to the heterogeneous nature of the patients. In order to build effective medical predictive models we need to address such heterogeneous nature during modeling and allow patients to have their own personalized models instead of using a one-size-fits-all model. However, building a personalized model for each patient is computationally expensive and the over-parametrization of the model makes it susceptible to the model overfitting problem. To address these challenges, we propose a novel approach called FactORized MUlti-task LeArning model (Formula), which learns the personalized model of each patient via a sparse multi-task learning method. The personalized models are assumed to share a low-rank representation, known as the base models. Formula is designed to simultaneously learn the base models as well as the personalized model of each patient, where the latter is a linear combination of the base models. We have performed extensive experiments to evaluate the proposed approach on a real medical data set. The proposed approach delivered superior predictive performance while the personalized models offered many useful medical insights.

Pang-Ning Tan

Papers

Support envelopes: a technique for exploring the structure of association patterns

A framework for joint community detection across multiple related networks

A Low Rank Weighted Graph Convolutional Approach to Weather Prediction

Macroscale patterns of synchrony identify complex relationships among spatial and temporal ecosystem drivers

FORMULA: FactORized MUlti-task LeArning for task discovery in personalized medical models