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

Data Mining - Concepts and Techniques.

Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects of "word of mouth" in the promotion of new products. Recently, motivated by the design of viral marketing strategies, Domingos and Richardson posed a fundamental algorithmic problem for such social network processes: if we can try to convince a subset of individuals to adopt a new product or innovation, and the goal is to trigger a large cascade of further adoptions, which set of individuals should we target?We consider this problem in several of the most widely studied models in social network analysis. The optimization problem of selecting the most influential nodes is NP-hard here, and we provide the first provable approximation guarantees for efficient algorithms. Using an analysis framework based on submodular functions, we show that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models; our framework suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.We also provide computational experiments on large collaboration networks, showing that in addition to their provable guarantees, our approximation algorithms significantly out-perform node-selection heuristics based on the well-studied notions of degree centrality and distance centrality from the field of social networks.

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Maximizing the Spread of Influence through a Social Network

The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

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Data Mining: Concepts and Techniques (2nd edition)

While the study of psychology has offered little in the way of explaining the creative process, Koestler examines the idea that we are at our most creative when rational thought is suspended--for example, in dreams and trancelike states. All who read The Act of Creation will find it a compelling and illuminating book.

The Act of Creation

For addressing the growing problem of junk E-mail on the Internet, this paper proposes an effective E-mail classifying and cleansing method in this paper. Incidentally, E-mail messages can be modelled as semi-structured documents consisting of a set of fields with pre-defined semantics and a number of variable length free-text fields. Our proposed method deals with both fields having pre-defined semantics as well as variable length free-text fields for obtaining higher accuracy. The main contributions of this work are two-fold. First, we present a new model based on the Neural Network (NN) for classifying personal E-mails. In particular, we treat E-mail files as a particular kind of plain text files, the implication being that our feature set is relatively large (since there are thousands of different terms in different E-mail files). Second, we propose the use of Principal Component Analysis (PCA) as a preprocessor of NN to reduce the data in terms of both size as well as dimensionality so that the input data become more classifiable and faster for the convergence of the training process used in the NN model. The results of our performance evaluation demonstrate that the proposed algorithm is indeed effective in performing filtering with reasonable accuracy.

On effective e-mail classification via neural networks

A large body of research on subgraph query processing on large networks assumes that a query is posed in the form of a connected graph. Unfortunately, end users in practice may not always have precise knowledge about the topological relationships between nodes in a query graph to formulate a connected query. In this demonstration, we present a novel graph querying paradigm called partial topology-based network search and a query processing system called panda to efficiently find top-k matches of a partial topology query (ptq) in a single machine. A ptq is a disconnected query graph containing multiple connected query components. ptqs allow an end user to formulate queries without demanding precise information about the complete topology of a query graph. We demonstrate various innovative features of panda and its promising performance.

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PANDA: a system for partial topology-based search on large networks

Learned indices have been proposed to replace classic index structures like B-Tree with machine learning (ML) models. They require to replace both the indices and query processing algorithms currently deployed by the databases, and such a radical departure is likely to encounter challenges and obstacles. In contrast, we propose a fundamentally different way of using ML techniques to improve on the query performance of the classic R-Tree without the need of changing its structure or query processing algorithms. Specifically, we develop reinforcement learning (RL) based models to decide how to choose a subtree for insertion and how to split a node when building an R-Tree, instead of relying on hand-crafted heuristic rules currently used by R-Tree and its variants. Experiments on real and synthetic datasets with up to more than 100 million spatial objects clearly show that our RL based index outperforms R-Tree and its variants in terms of query processing time.

A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments.

Trajectory data has been widely used in various applications, including taxi services, traffic management, mobility analysis, etc. It is usually collected at a sensor's side in real time and corresponds to a sequence of sampled points. Constrained by the storage and/or network bandwidth of a sensor, it is common to simplify raw trajectory data when it is collected by dropping some sampled points. Many algorithms have been proposed for the error-bounded online trajectory simplification (EB-OTS) problem, which is to drop as many points as possible subject to that the error is bounded by an error tolerance. Nevertheless, these existing algorithms rely on pre-defined rules for decision making during the trajectory simplification process and there is no theoretical ground supporting their effectiveness. In this paper, we propose a multi-agent reinforcement learning method called MARL4TS for EB-OTS. MARL4TS involves two agents for different decision making problems during the trajectory simplification processes. Besides, MARL4TS has its objective equivalent to that of the EB-OTS problem, which provides some theoretical ground of its effectiveness. We conduct extensive experiments on real-world trajectory datasets, which verify that MARL4TS outperforms all existing algorithms in effectiveness and provides competitive efficiency.

Error-Bounded Online Trajectory Simplification with Multi-Agent Reinforcement Learning

Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures real-time traffic conditions in road networks provides promising new opportunities to identify the traffic bottlenecks. In this paper, we define this problem as trajectory-driven traffic bottleneck identification: Given a road network R, a trajectory database T , find a representative set of seed edges of size K of traffic bottlenecks that influence the highest number of road segments not in the seed set. We show that this problem is NP-hard and propose a framework to find the traffic bottlenecks as follows. First, a traffic spread model is defined which represents changes in traffic volume for each road segment over time. Then, the traffic diffusion probability between two connected segments and the residual ratio of traffic volume for each segment can be computed using historical trajectory data. We then propose two different algorithmic approaches to solve the problem. The first one is a best-first algorithm BF, with an approximation ratio of 1-1/e. To further accelerate the identification process in larger datasets, we also propose a sampling-based greedy algorithm SG. Finally, comprehensive experiments using three different datasets compare and contrast various solutions, and provide insights into important efficiency and effectiveness trade-offs among the respective methods.

Gao Cong

Papers

On effective e-mail classification via neural networks

PANDA: a system for partial topology-based search on large networks

A Reinforcement Learning Based R-Tree for Spatial Data Indexing in Dynamic Environments.

Error-Bounded Online Trajectory Simplification with Multi-Agent Reinforcement Learning

Let Trajectories Speak Out the Traffic Bottlenecks