OpenRaiser/Intern-Atlas huggingface dataset:
Coupled behavior analysis for capturing coupling relationships in group-based market manipulations
Authors: Yin Song, Longbing Cao, Xindong Wu, Gang Wei, Wu Ye, Wei Ding
Venue: KDD
Year: 2012

In stock markets, an emerging challenge for surveillance is that a group of hidden manipulators collaborate with each other to manipulate the price movement of securities. Recently, the coupled hidden Markov model (CHMM)-based coupled behavior analysis (CBA) has been proposed to consider the coupling relationships in the above group-based behaviors for manipulation detection. From the modeling perspective, however, this requires overall aggregation of the behavioral data to cater for the CHMM modeling, which does not differentiate the coupling relationships presented in different forms within the aggregated behaviors and degrade the capability for further anomaly detection. Thus, this paper suggests a general CBA framework for detecting group-based market manipulation by capturing more comprehensive couplings and proposes two variant implementations, which are hybrid coupling (HC)-based and hierarchical grouping (HG)-based respectively. The proposed framework consists of three stages. The first stage, qualitative analysis, generates possible qualitative coupling relationships between behaviors with or without domain knowledge. In the second stage, quantitative representation of coupled behaviors is learned via proper methods. For the third stage, anomaly detection algorithms are proposed to cater for different application scenarios. Experimental results on data from a major Asian stock market show that the proposed framework outperforms the CHMM-based analysis in terms of detecting abnormal collaborative market manipulations. Additionally, the two different implementations are compared with their effectiveness for different application scenarios.
On nested palindromes in clickstream data
Authors: Michel Speiser, Gianluca Antonini, Abderrahim Labbi, Juliana Sutanto
Venue: KDD
Year: 2012

In this paper we discuss an interesting and useful property of clickstream data. Often a visit includes repeated views of the same page. We show that in three real datasets, sampled from the websites of technology and consulting groups and a news broadcaster, page repetitions occur for the majority as a very specific structure, namely in the form of nested palindromes. This can be explained by the widespread use of features which are available in any web browser: the "refresh" and "back" buttons. Among the types of patterns which can be mined from sequence data, many either stumble if symbol repetitions are involved, or else fail to capture interesting aspects related to symbol repetitions. In an attempt to remedy this, we characterize the palindromic structures, and discuss possible ways of making use of them. One way is to pre-process the sequence data by explicitly inserting these structures, in order to obtain a richer output from conventional mining algorithms. Another application we discuss is to use the information directly, in order to analyze certain aspects of the website under study. We also provide the simple linear-time algorithm which we developed to identify and extract the structures from our data.
Understanding users' satisfaction for search engine evaluation
Authors: Gordon Sun
Venue: KDD
Year: 2012

To fulfill users' search needs, the search engine must have good performance, easy-to-use functionalities, and good search result quality. Search quality evaluation becomes challenging when users' satisfaction may not be able to judge by a single search and even within a single search judgments from various sources are not consistent. In this talk, I will discuss how user's satisfaction is decomposed into different components in general, and how we measure them with various means - human judgment, automatic computation with query log, and outsourcing, and their pros and cons with operational implications. For an outlook, I will postulate potential evaluation approaches for a better user's satisfaction.
Integrating meta-path selection with user-guided object clustering in heterogeneous information networks
Authors: Yizhou Sun, Brandon Norick, Jiawei Han, Xifeng Yan, Philip S. Yu, Xiao Yu
Venue: KDD
Year: 2012

Real-world, multiple-typed objects are often interconnected, forming heterogeneous information networks. A major challenge for link-based clustering in such networks is its potential to generate many different results, carrying rather diverse semantic meanings. In order to generate desired clustering, we propose to use meta-path, a path that connects object types via a sequence of relations, to control clustering with distinct semantics. Nevertheless, it is easier for a user to provide a few examples ("seeds") than a weighted combination of sophisticated meta-paths to specify her clustering preference. Thus, we propose to integrate meta-path selection with user-guided clustering to cluster objects in networks, where a user first provides a small set of object seeds for each cluster as guidance. Then the system learns the weights for each meta-path that are consistent with the clustering result implied by the guidance, and generates clusters under the learned weights of meta-paths. A probabilistic approach is proposed to solve the problem, and an effective and efficient iterative algorithm, PathSelClus, is proposed to learn the model, where the clustering quality and the meta-path weights are mutually enhancing each other. Our experiments with several clustering tasks in two real networks demonstrate the power of the algorithm in comparison with the baselines.
Unsupervised feature selection for linked social media data
Authors: Jiliang Tang, Huan Liu
Venue: KDD
Year: 2012

The prevalent use of social media produces mountains of unlabeled, high-dimensional data. Feature selection has been shown effective in dealing with high-dimensional data for efficient data mining. Feature selection for unlabeled data remains a challenging task due to the absence of label information by which the feature relevance can be assessed. The unique characteristics of social media data further complicate the already challenging problem of unsupervised feature selection, (e.g., part of social media data is linked, which makes invalid the independent and identically distributed assumption), bringing about new challenges to traditional unsupervised feature selection algorithms. In this paper, we study the differences between social media data and traditional attribute-value data, investigate if the relations revealed in linked data can be used to help select relevant features, and propose a novel unsupervised feature selection framework, LUFS, for linked social media data. We perform experiments with real-world social media datasets to evaluate the effectiveness of the proposed framework and probe the working of its key components.
Discovering lag intervals for temporal dependencies
Authors: Liang Tang, Tao Li, Larisa Shwartz
Venue: KDD
Year: 2012

Time lag is a key feature of hidden temporal dependencies within sequential data. In many real-world applications, time lag plays an essential role in interpreting the cause of discovered temporal dependencies. Traditional temporal mining methods either use a predefined time window to analyze the item sequence, or employ statistical techniques to simply derive the time dependencies among items. Such paradigms cannot effectively handle varied data with special properties, e.g., the interleaved temporal dependencies.
Intelligent advertising framework for digital signage
Authors: Phil Tian, Addicam V. Sanjay, Kunapareddy Chiranjeevi, Shahzad Malik Malik
Venue: KDD
Year: 2012

How to realize targeted advertising in digital signage is an interesting question. This paper proposed an Intelligent Advertising Framework (IAF), which pioneers the integration of Anonymous Viewer Analytics (AVA) and Data Mining technologies to achieve Targeted and interactive Advertising. IAF correlates AVA viewership information with point-of-sale (POS) data, and establishes a link between the response time to an ad by a certain demographic group and the effect on the sale of the advertised product. With the advertising models learned based on this correlation, IAF can provide advertisers and retailers with intelligence to show the right ads to right audience in right location at right time. Preliminary results indicate that IAF will greatly improve the effect and utility of advertising and maximize the Return on Investment (ROI) of advertisers and retailers. The demo shows Intel's leadership regarding intelligent advertising in the Digital Signage industry.
Learning from crowds in the presence of schools of thought
Authors: Yuandong Tian, Jun Zhu
Venue: KDD
Year: 2012

Crowdsourcing has recently become popular among machine learning researchers and social scientists as an effective way to collect large-scale experimental data from distributed workers. To extract useful information from the cheap but potentially unreliable answers to tasks, a key problem is to identify reliable workers as well as unambiguous tasks. Although for objective tasks that have one correct answer per task, previous works can estimate worker reliability and task clarity based on the single gold standard assumption, for tasks that are subjective and accept multiple reasonable answers that workers may be grouped into, a phenomenon called schools of thought, existing models cannot be trivially applied. In this work, we present a statistical model to estimate worker reliability and task clarity without resorting to the single gold standard assumption. This is instantiated by explicitly characterizing the grouping behavior to form schools of thought with a rank-1 factorization of a worker-task groupsize matrix. Instead of performing an intermediate inference step, which can be expensive and unstable, we present an algorithm to analytically compute the sizes of different groups. We perform extensive empirical studies on real data collected from Amazon Mechanical Turk. Our method discovers the schools of thought, shows reasonable estimation of worker reliability and task clarity, and is robust to hyperparameter changes. Furthermore, our estimated worker reliability can be used to improve the gold standard prediction for objective tasks.
UFIMT: an uncertain frequent itemset mining toolbox
Authors: Yongxin Tong, Lei Chen, Philip S. Yu
Venue: KDD
Year: 2012

In recent years, mining frequent itemsets over uncertain data has attracted much attention in the data mining community. Unlike the corresponding problem in deterministic data, the frequent itemset under uncertain data has two different definitions: the expected support-based frequent itemset and the probabilistic frequent itemset. Most existing works only focus on one of the definitions and no comprehensive study is conducted to compare the two different definitions. Moreover, due to lacking the uniform implementation platform, existing solutions for the same definition even generate inconsistent results. In this demo, we present a demonstration called as UFIMT (underline Uncertain Frequent Itemset Mining Toolbox) which not only discovers frequent itemsets over uncertain data but also compares the performance of different algorithms and demonstrates the relationship between different definitions. In this demo, we firstly present important techniques and implementation skills of the mining problem, secondly, we show the system architecture of UFIMT, thirdly, we report an empirical analysis on extensive both real and synthetic benchmark data sets, which are used to compare different algorithms and to show the close relationship between two different frequent itemset definitions, and finally we discuss some existing challenges and new findings.
Bayesian relational data analysis
Authors: Naonori Ueda
Venue: KDD
Year: 2012

Recently there have been many collections of relational data in diverse areas such as the internet, social networks, customer shopping records, bioinformatics, etc. The main goal of the relational data analysis is to discover latent structure from the data. The conventional data mining algorithms based on exhaustive enumeration have an inherent limitation for this purpose because of the combinatorial nature of the methods. In contrast, in machine learning a lot of statistical models have been proposed for the relational data analysis. In this talk, first I will review the statistical approach, especially Bayesian approach, for the relational data analysis with recent advancements in machine learning literature. Then, as a future research I will also talk about a statistical approach for combining multiple relational data.
TM-LDA: efficient online modeling of latent topic transitions in social media
Authors: Yu Wang, Eugene Agichtein, Michele Benzi
Venue: KDD
Year: 2012

Latent topic analysis has emerged as one of the most effective methods for classifying, clustering and retrieving textual data. However, existing models such as Latent Dirichlet Allocation (LDA) were developed for static corpora of relatively large documents. In contrast, much of the textual content on the web, and especially social media, is temporally sequenced, and comes in short fragments, including microblog posts on sites such as Twitter and Weibo, status updates on social networking sites such as Facebook and LinkedIn, or comments on content sharing sites such as YouTube. In this paper we propose a novel topic model, Temporal-LDA or TM-LDA, for efficiently mining text streams such as a sequence of posts from the same author, by modeling the topic transitions that naturally arise in these data. TM-LDA learns the transition parameters among topics by minimizing the prediction error on topic distribution in subsequent postings. After training, TM-LDA is thus able to accurately predict the expected topic distribution in future posts. To make these predictions more efficient for a realistic online setting, we develop an efficient updating algorithm to adjust the topic transition parameters, as new documents stream in. Our empirical results, over a corpus of over 30 million microblog posts, show that TM-LDA significantly outperforms state-of-the-art static LDA models for estimating the topic distribution of new documents over time. We also demonstrate that TM-LDA is able to highlight interesting variations of common topic transitions, such as the differences in the work-life rhythm of cities, and factors associated with area-specific problems and complaints.
A density-based clustering structure mining algorithm for data streams
Authors: Huan Wang, Yanwei Yu, Qin Wang, Yadong Wan
Venue: KDD
Year: 2012

Today, advances in hardware and storage techniques demand for automatically data mining on data streams. Clustering analysis is an importance tool on data streams mining. Though density-based clustering algorithms on data streams now could discover clusters of arbitrary shapes, their effectiveness are depended on parameters settings. Also global parameters used in these algorithms limit their ability in discovering overlapping clusters. In this paper, we propose a novel density-based clustering structure mining algorithm for data streams---OPCluStream. It could adaptively discover clusters of arbitrary shapes and overlapping clusters. Satisfying one-pass constraint, OPCluStream uses a tree topology to index points on which points link to other related ones using pointers directionally. This tree topology records relationships among points, which represent clustering results including a broad range of Eps settings and could discover clusters through a transformation to clustering structure. Clustering structure is equivalent to the index structure and convenient to be used. In addition, OPCluStream has a high efficiency on clustering since a usage of tree topology in points' index and a designed limited computing area when new points added to data streams. A number of experiments on synthetic and real data sets illustrate the effectiveness, efficiency and insights provided by our method.
Magnet community identification on social networks
Authors: Guan Wang, Yuchen Zhao, Xiaoxiao Shi, Philip S. Yu
Venue: KDD
Year: 2012

Social communities connect people of similar interests together and play essential roles in social network applications. Examples of such communities include people who like the same objects on Facebook, follow common subjects on Twitter, or join similar groups on LinkedIn. Among communities, we notice that some of them are {\em magnetic} to people. A {\em magnet community} is such a community that attracts significantly more people's interests and attentions than other communities of similar topics. With the explosive number of self-formed communities in social networks, one important demand is to identify magnet communities for users. This can not only track attractive communities, but also help improve user experiences and increase their engagements, e.g., the login frequencies and user-generated-content qualities. In this paper, we initiate the study of magnet community identification problem. First we observe several properties of magnet communities, such as attention flow, attention qualify, and attention persistence. Second, we formalize these properties with the combination of community feature extraction into a graph ranking formulation based on constraint quadratic programming. In details, we treat communities of a network as super nodes, and their interactions as links among those super nodes. Therefore, a network of communities is defined. We extract community's magnet features from heterogeneous sources, i.e., a community's standalone features and its dependency features with other communities. A graph ranking model is formulated given these features. Furthermore, we define constraints reflecting communities' magnet properties to regularize the model. We demonstrate the effectiveness of our framework on real world social network data.
Ensembles and model delivery for tax compliance
Authors: Graham Williams
Venue: KDD
Year: 2012

Revenue authorities characteristically have large stores of historic audit data, with outcomes, ready for analysis. The Australian Taxation Office established one of the largest data mining teams in Australia in 2004 as a foundation to becoming a knowledge-based organization. Today, every tax return lodged in Australia is risk assessed by one or more models developed through data mining, generally based on historic data. We observe that any of the traditional modeling approaches, particularly including random forests, generally deliver similar models in terms of accuracy. We take advantage of combining different model types and modeling approaches for risk scoring, and in particular report on recent research that increases the diversity of trees that make up a random forest. We also review, in a practical context, how such models are evaluated and delivered.
On "one of the few" objects
Authors: You Wu, Pankaj K. Agarwal, Chengkai Li, Jun Yang, Cong Yu
Venue: KDD
Year: 2012

Objects with multiple numeric attributes can be compared within any "subspace" (subset of attributes). In applications such as computational journalism, users are interested in claims of the form: Karl Malone is one of the only two players in NBA history with at least 25,000 points, 12,000 rebounds, and 5,000 assists in one's career. One challenge in identifying such "one-of-the-k" claims (k = 2 above) is ensuring their "interestingness". A small k is not a good indicator for interestingness, as one can often make such claims for many objects by increasing the dimensionality of the subspace considered. We propose a uniqueness-based interestingness measure for one-of-the-few claims that is intuitive for non-technical users, and we design algorithms for finding all interesting claims (across all subspaces) from a dataset. Sometimes, users are interested primarily in the objects appearing in these claims. Building on our notion of interesting claims, we propose a scheme for ranking objects and an algorithm for computing the top-ranked objects. Using real-world datasets, we evaluate the efficiency of our algorithms as well as the advantage of our object-ranking scheme over popular methods such as Kemeny optimal rank aggregation and weighted-sum ranking.
Mining top-K high utility itemsets
Authors: Cheng-Wei Wu, Bai-En Shie, Vincent S. Tseng, Philip S. Yu
Venue: KDD
Year: 2012

Mining high utility itemsets from databases is an emerging topic in data mining, which refers to the discovery of itemsets with utilities higher than a user-specified minimum utility threshold min_util. Although several studies have been carried out on this topic, setting an appropriate minimum utility threshold is a difficult problem for users. If min_util is set too low, too many high utility itemsets will be generated, which may cause the mining algorithms to become inefficient or even run out of memory. On the other hand, if min_util is set too high, no high utility itemset will be found. Setting appropriate minimum utility thresholds by trial and error is a tedious process for users. In this paper, we address this problem by proposing a new framework named top-k high utility itemset mining, where k is the desired number of high utility itemsets to be mined. An efficient algorithm named TKU (Top-K Utility itemsets mining) is proposed for mining such itemsets without setting min_util. Several features were designed in TKU to solve the new challenges raised in this problem, like the absence of anti-monotone property and the requirement of lossless results. Moreover, TKU incorporates several novel strategies for pruning the search space to achieve high efficiency. Results on real and synthetic datasets show that TKU has excellent performance and scalability.
VOXSUP: a social engagement framework
Authors: Yusheng Xie, Daniel Honbo, Alok N. Choudhary, Kunpeng Zhang, Yu Cheng, Ankit Agrawal
Venue: KDD
Year: 2012

Social media websites are currently central hubs on the Internet. Major online social media platforms are not only places for individual users to socialize but are increasingly more important as channels for companies to advertise, public figures to engage, etc. In order to optimize such advertising and engaging efforts, there is an emerging challenge for knowledge discovery on today's Internet. The goal of knowledge discovery is to understand the entire online social landscape instead of merely summarizing the statistics. To answer this challenge, we have created VOXSUP as a unified social engagement framework. Unlike most existing tools, VOXSUP not only aggregates and filters social data from the Internet, but also provides what we call Voxsupian Knowledge Discovery (VKD). VKD consists of an almost human-level understanding of social conversations at any level of granularity from a single comment sentiment to multi-lingual inter-platform user demographics. Here we describe the technologies that are crucial to VKD, and subsequently go beyond experimental verification and present case studies from our live VOXSUP system.
Scalable misbehavior detection in online video chat services
Authors: Xinyu Xing, Yu-Li Liang, Sui Huang, Hanqiang Cheng, Richard Han, Qin Lv, Xue Liu, Shivakant Mishra, Yi Zhu
Venue: KDD
Year: 2012

The need for highly scalable and accurate detection and filtering of misbehaving users and obscene content in online video chat services has grown as the popularity of these services has exploded in popularity. This is a challenging problem because processing large amounts of video is compute intensive, decisions about whether a user is misbehaving or not must be made online and quickly, and moreover these video chats are characterized by low quality video, poorly lit scenes, diversity of users and their behaviors, diversity of the content, and typically short sessions. This paper presents EMeralD, a highly scalable system for accurately detecting and filtering misbehaving users in online video chat applications. EMeralD substantially improves upon the state-of-the-art filtering mechanisms by achieving much lower computational cost and higher accuracy. We demonstrate EMeralD's improvement via experimental evaluations on real-world data sets obtained from Chatroulette.com.