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

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)

The increasing availability of GPS-enabled devices is changing the way people interact with the Web, and brings us a large amount of GPS trajectories representing people's location histories. In this paper, based on multiple users' GPS trajectories, we aim to mine interesting locations and classical travel sequences in a given geospatial region. Here, interesting locations mean the culturally important places, such as Tiananmen Square in Beijing, and frequented public areas, like shopping malls and restaurants, etc. Such information can help users understand surrounding locations, and would enable travel recommendation. In this work, we first model multiple individuals' location histories with a tree-based hierarchical graph (TBHG). Second, based on the TBHG, we propose a HITS (Hypertext Induced Topic Search)-based inference model, which regards an individual's access on a location as a directed link from the user to that location. This model infers the interest of a location by taking into account the following three factors. 1) The interest of a location depends on not only the number of users visiting this location but also these users' travel experiences. 2) Users' travel experiences and location interests have a mutual reinforcement relationship. 3) The interest of a location and the travel experience of a user are relative values and are region-related. Third, we mine the classical travel sequences among locations considering the interests of these locations and users' travel experiences. We evaluated our system using a large GPS dataset collected by 107 users over a period of one year in the real world. As a result, our HITS-based inference model outperformed baseline approaches like rank-by-count and rank-by-frequency. Meanwhile, when considering the users' travel experiences and location interests, we achieved a better performance beyond baselines, such as rank-by-count and rank-by-interest, etc.

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Mining interesting locations and travel sequences from GPS trajectories

Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions. We believe that frequent pattern mining research has substantially broadened the scope of data analysis and will have deep impact on data mining methodologies and applications in the long run. However, there are still some challenging research issues that need to be solved before frequent pattern mining can claim a cornerstone approach in data mining applications.

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Frequent pattern mining: current status and future directions

The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.

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Trajectory Data Mining: An Overview

HPC systems are inherently complex, both to work with and to maintain. Trying to anticipate a sudden event, such as component failure or how the system will react to a newly installed module, is too large and convoluted of a problem for a single person or group of people to solve manually. In this paper, we attempt to explore the causal relationships present amongst sensors and monitoring data found in these kinds of machines. The intent of this study is to both better understand how different components and modules of the machines interact with each other, as well as get a better understanding of how a change in one part of the machine effects another part. To achieve this, we apply both a Bayesian network and logistic regression, in conjunction with a causal graph generator (TETRAD), on sensor data generated from the Trinity supercomputer in Los Alamos, NM. In particular, the data that was examined in this study focused on data from 4 slot-level sensors and 3 row-level sensors. It was found that, while these sensors do contain causal structure by themselves, they do not seem to makeup the entire causal structure, only a portion of it. The presence of latent variables, as well as possibly more interconnections (i.e. causal relationships) between each of the sensors, are likely having an effect on the predictive accuracy of the Bayesian network and logistic regression experiments conducted in this study. Therefore, it is recommended, for future work, that more experiments are conducted involving more sensors and possibly other relevant data.

Causal Relationships amongst Sensors in the Trinity Supercomputer: work in progress

Most e-commerce websites (e.g., Amazon and TripAdvisor) show their users an initial set of useful product reviews. These reviews allow users to form a general idea about the product’s characteristics. The usefulness of a review is mainly based on a score that the website users provide. Studies have shown that this score is not a good indicator of a review’s actual helpfulness. Nonetheless, most past works still use it to classify a review as helpful or not. With the growing number of reviews, finding those helpful ones is a challenging task. In this work, we propose NovRev, a new unsupervised approach to recommend a personalized subset of unread useful reviews for those users looking to increase their knowledge about a product. NovRev considers an initial set of reviews as a context and recommends reviews that increase the product’s information. We have extensively tested NovRev against five baseline methods, using eight real-life datasets from different product domains. The results show that NovRev can recommend novel, relevant, and diverse reviews while covering more information about the product.

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Recommending novel and relevant reviews to expand users’ knowledge about a product


 The study’s objective was to investigate the use of Internet of Things (IoT) biosensors using Long Range Wide Area Network (LoRa-WAN) communication to compare the foraging behavior of two desert adapted cattle breeds. Two groups of mature Brangus (BA) and Raramuri Criollo (RC) cows were introduced into a small irrigated pasture (38ha) at the New Mexico State University’s Clayton Livestock Research Center during Dec-Jan, 2021 (41-days). The real-time position and activity count data (15- and 1-minute interval, respectively) were retrieved continuously from 4 BA and 5 RC cows collared with commercially available trackers (Abeeway Industrial-trackers). Response variables derived from GPS records included daily distance traveled (Dist) and daily area explored (Area). Response variables derived from accelerometers included daily percentage motion (%Act) and cumulative activity data (CumAct). All variables were grouped into six consecutive weekly periods and analyzed by a repeated measures mixed model that included the fixed effects of BREED, WEEK, and the BREED*WEEK interaction in a completely randomized design. A significant (P< 0.01) BREED*WEEK interaction was detected on all GPS-derived variables, with RC exhibiting a trend to explore larger areas than BA (P=0.06). For the accelerometer-derived variables, the BREED*WEEK interaction was only significant (P=0.01) for CumAct. Overall, BA spent a larger percentage of the day active (%Act; BA= 76.74 vs. RC= 73.16; P=0.02), while investing a significantly greater overall activity (CumAct; BA= 8816.63 vs. RC= 6311.31; P=0.03) compared to RC cows. The insensitivity of GPS-derived variables is hypothesized to be the result of aggregating errors associated with less frequent GPS location measurements coupled with position errors (~5m) and a low success rate of data packet transmission (~40%). The spatial scale of the study is hypothesized to have influenced the foraging behavior differences that were observed between the breeds, and the better performance of the accelerometer vs. GPS-derived variables.

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PSXIII-7 Field Testing of Lora-wan Sensors for Real-Time Tracking and Biosensing of Brangus and Raramuri Criollo Cattle Grazing a Small Pasture

Consider a scientist who wants to explore multiple data sets to select the relevant ones for further analysis. Since the vi- sualization real estate may put a stringent constraint on how much detail can be presented to this user in a single page, ef- fective table summarization techniques are needed to create summaries that are both sufficiently small and effective in communicating the available content. In this paper, we first argue that table summarization can benefit from knowledge about acceptable value clustering alternatives for clustering the values in the database. We formulate the problem of table summarization with the help of value lattices. We then provide a framework to express alternative clustering strategies and to account for various utility measures (such as information loss) in assessing different summarization al- ternatives. Based on this interpretation, we introduce three preference criteria, max-min-util (cautious), max-sum-util (cumulative), and pareto-util, for the problem of table sum- marization. To tackle with the inherent complexity, we rely on the properties of the fuzzy interpretation to further de- velop a novel ranked set cover based evaluation mechanism (RSC). These are brought together in an AlphaSum, table summarization system. Experimental evaluations showed that RSC improves both execution times and the summary qualities in AlphaSum, by pruning the search space more effectively than the existing solutions.

/pdf/alphasum-size-constrained-table-summarization-using-3rirkj4cj7.pdf

AlphaSum: Size-Constrained Table Summarization using

An overall rating cannot reveal the details of user’s preferences toward each feature of a product. One widespread practice of e-commerce websites is to provide ratings on predefined aspects of the product and user-generated reviews. Most recent multi-criteria works employ aspect preferences of users or user reviews to understand the opinions and behavior of users. However, these works fail to learn how users correlate these information sources when users express their opinion about an item. In this work, we present Multi-task & Multi-Criteria Review-based Rating (MMCRR), a framework to predict the overall ratings of items by learning how users represent their preferences when using multi-criteria ratings and text reviews. We conduct extensive experiments with three real-life datasets and six baseline models. The results show that MMCRR can reduce prediction errors while learning features better from the data.

Huiping Cao

Papers

Causal Relationships amongst Sensors in the Trinity Supercomputer: work in progress

Recommending novel and relevant reviews to expand users’ knowledge about a product

PSXIII-7 Field Testing of Lora-wan Sensors for Real-Time Tracking and Biosensing of Brangus and Raramuri Criollo Cattle Grazing a Small Pasture

AlphaSum: Size-Constrained Table Summarization using

Multi-criteria and Review-Based Overall Rating Prediction