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.

/pdf/frequent-pattern-mining-current-status-and-future-directions-31jxo4xdkd.pdf

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

Graphs have been widely used to represent objects and ob- ject connections in applications such as the Web, social net- works, and citation networks. Mining influence relationships from graphs has gained interests in recent years because pro- viding influence information about the object connections in graphs can facilitate graph exploration, graph search, and connection recommendations. In this paper, we study the problem of detecting influence aspects, on which objects are connected, and influence degree (or influence strength), with which one graph node influences another graph node on a given aspect. Existing techniques focus on inferring either the influence degrees or influence types from graphs. We propose two generative Aspect Influence Models, OAIM and LAIM, to detect both influence aspects and influence de- grees. These models utilize the topological structure of the graphs, the text content associated with objects, and the con- text in which the objects are connected. We compare these two models with one baseline approach which considers only the text content associated with objects. The empirical stud- ies on citation graphs and networks of users from Twitter show that our models can discover more effective results than the baseline approach.

https://epubs.siam.org/doi/pdf/10.1137/1.9781611973440.94

Detecting Influence Relationships from Graphs.

Periodic Pattern Discovery from Trajectories of Moving Objects

It is very helpful for a user to get a moderate amount of information highly related to his/her immediate context (e.g., location, time, discussion topics) during the exploration of digital object collections (e.g., articles, web pages, blogs). For instance, in investigating a research topic, a researcher may be very interested in finding articles that are most related to the articles he/she already read on this topic, which we consider as “context” in this paper. To facilitate users’ exploration, we introduce the problem of discovering Contextaware Influential Objects (CIO) from a collection of digital objects with influence relationships. Although there is a large amount of work in detecting direct influence degree between objects to denote how strong an object influences others, very few works utilize such direct influence to find influential objects for a context. To discover CIOs for a context consisting of several objects of a user’s interest, the first challenge is to meaningfully measure the collective influence of an object over a context considering both the direct influence and the indirectly derived influence, which is not taken into consideration by most “query by example” approaches. We propose an aggregation framework to formulate the collective influence among objects by leveraging both direct and indirect influence. The second challenge is to discover CIOs efficiently. We present three approaches to calculate collective influence of an object over a context from an influence graph. In particular, the first approach utilizes the breadth-first-search paradigm; the other approaches make use of the topological sorting of graph nodes and perform context-aware search using push and pull mechanisms. We show experimental results on real datasets to demonstrate the effectiveness and efficiency of the proposed methodologies.

https://epubs.siam.org/doi/pdf/10.1137/1.9781611972825.67

Discovering Context-aware Influential Objects.

In XML Data Integration, data/metadata merging and query processing are indispensable. Specifically, merging integrates multiple disparate (heterogeneous and autonomous) input data sources together for further usage, while query processing is one main reason why the data need to be integrated in the first place. Besides, when supported with appropriate user feedback techniques, queries can also provide contexts in which conflicts among the input sources can be interpreted and resolved. The flexibility of XML structure provides opportunities for alleviating some of the difficulties that other less flexible data types face in the presence of uncertainty; yet, this flexibility also introduces new challenges in merging multiple sources and query processing over integrated data. In this chapter, we discuss two alternative ways XML data/schema can be integrated: conflict-eliminating (where the result is cleaned from any conflicts that the different sources might have with each other) and conflict-preserving (where the resulting XML data or XML schema captures the alternative interpretations of the data). We also present techniques for query processing over integrated, possibly imprecise, XML data, and cover strategies that can be used for resolving underlying conflicts.

XML Data Integration: Merging, Query Processing and Conflict Resolution

Most recommender services help individual users by recommending items within a single category based on the items' overall ratings. However, this may not be sufficient for group activities. For example, a group of friends using an online travel website look for a weekend getaway package (with hotel and restaurant), where the group members have different preferences over the characteristics (e.g., price, service, ambient) of these items. We call items with multiple characteristics as multi-criteria items. The items may come from different categories (e.g., hotel, restaurant). This paper proposes a novel problem of recommending packages of multi-criteria items to a group of users by leveraging users' preferences over categories. As far as we know, our work is the first paper studying this problem. We propose two models to measure the preference of a group to a package. The first model utilizes users' preferences for all the items and all categories, while the second model further leverages the influence of different group members to user preferences. We further introduce a new metric, to measure the fairness of the recommendations to different group members. We present an approach that utilizes co-clustering to incorporate items' characteristics in the calculation of user preferences and creating recommendations. Finally, we conduct extensive experiments with three real datasets. The experiments show that the second model can find packages that balance better the preferences of all the group members.

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Huiping Cao

Papers

Detecting Influence Relationships from Graphs.

Periodic Pattern Discovery from Trajectories of Moving Objects

Discovering Context-aware Influential Objects.

XML Data Integration: Merging, Query Processing and Conflict Resolution

Recommending Packages of Multi-Criteria Items to Groups