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.

/pdf/maximizing-the-spread-of-influence-through-a-social-network-47y1ehuvwi.pdf

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.

/pdf/data-mining-concepts-and-techniques-2nd-edition-zx1xx1f5e3.pdf

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

Spatiotemporal Topic Detection from Social Media.

Sparse subspace clustering (SSC) is an effective approach to cluster high-dimensional data. However, how to adaptively select the number of clusters/eigenvectors for different data sets, especially when the data are corrupted by noise, is a big challenge in SSC and also an open problem in field of data mining. In this paper, considering the fact that the eigenvectors are robust to noise, we develop a self-adaptive search method to select cluster number for SSC by exploiting the cluster-separation information from eigenvectors. Our method solves the problem by identifying the cluster centers over eigenvectors. We first design a new density based metric, called centrality coefficient gap, to measure such separation information, and estimate the cluster centers by maximizing the gap. After getting the cluster centers, it is straightforward to group the remaining points into respective clusters which contain their nearest neighbors with higher density. This leads to a new clustering algorithm in which the final randomly initialized k-means stage in traditional SSC is eliminated. We theoretically verify the correctness of the proposed method on noise-free data. Extensive experiments on synthetic and real-world data corrupted by noise demonstrate the robustness and effectiveness of the proposed method comparing to the well-established competitors.

Robust Self-Tuning Sparse Subspace Clustering

Selectivity estimation is a fundamental database task, which has been studied for decades. A recent trend is to use deep learning methods for selectivity estimation. Deep autoregressive models have been reported to achieve excellent accuracy. However, if the relation has continuous attributes with large domain sizes, the search space of query inference on deep autoregressive models can be very large, resulting in inaccurate estimation and inefficient inference. To address this challenge, we propose a new model that integrates multiple Gaussian mixture models and a deep autoregressive model. On the one hand, Gaussian mixture models can fit the distribution of continuous attributes and reduce their domain sizes. On the other hand, deep autoregressive model can learn the joint data distribution with reduced domain attributes. In experiments, we compare with multiple baselines on 4 real-world datasets containing continuous attributes, and the experimental results demonstrate that our model can achieve up to 20 times higher accuracy than the second best estimators, while using less space and inference time.

Unsupervised Selectivity Estimation by Integrating Gaussian Mixture Models and an Autoregressive Model

Most supervised geographic mapping methods with very-high-resolution (VHR) images are designed for a specific task, leading to high label-dependency and inadequate task-generality. Additionally, the lack of socio-economic information in VHR images limits their applicability to social/human-related geographic studies. To resolve these two issues, we propose an unsupervised multi-modal geographic representation learning framework (MMGR) using both VHR images and points-of-interest (POIs), to learn representations (regional vector embeddings) carrying both the physical and socio-economic properties of the geographies. In MMGR, we employ an intra-modal and an inter-modal contrastive learning module, in which the former deeply mines visual features by contrasting different VHR image augmentations, while the latter fuses physical and socio-economic features by contrasting VHR image and POI features. Extensive experiments are performed in two study areas (Shanghai and Wuhan in China) and three relevant while distinctive geographic mapping tasks (i.e., mapping urban functional distributions, population density, and gross domestic product), to verify the superiority of MMGR. The results demonstrate that the proposed MMGR considerably outperforms seven competitive baselines in all three tasks, which indicates its effectiveness in fusing VHR images and POIs for multiple geographic mapping tasks. Furthermore, MMGR is a competent pre-training method to help image encoders understand multi-modal geographic information, and it can be further strengthened by fine-tuning even with a few labeled samples. The source code is released at https://github.com/bailubin/MMGR. 

Geographic mapping with unsupervised multi-modal representation learning from VHR images and POIs

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

Spatiotemporal Topic Detection from Social Media.

Robust Self-Tuning Sparse Subspace Clustering

Unsupervised Selectivity Estimation by Integrating Gaussian Mixture Models and an Autoregressive Model

Geographic mapping with unsupervised multi-modal representation learning from VHR images and POIs

Let Trajectories Speak Out the Traffic Bottlenecks.