Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 12 of 12
Full-Text Articles in Physical Sciences and Mathematics
Predicting Response In Mobile Advertising With Hierarchical Importance-Aware Factorization Machine, Richard Jayadi Oentaryo, Ee Peng Lim, Jia Wei Low, David Lo, Michael Finegold
Predicting Response In Mobile Advertising With Hierarchical Importance-Aware Factorization Machine, Richard Jayadi Oentaryo, Ee Peng Lim, Jia Wei Low, David Lo, Michael Finegold
David LO
Mobile advertising has recently seen dramatic growth, fueled by the global proliferation of mobile phones and devices. The task of predicting ad response is thus crucial for maximizing business revenue. However, ad response data change dynamically over time, and are subject to cold-start situations in which limited history hinders reliable prediction. There is also a need for a robust regression estimation for high prediction accuracy, and good ranking to distinguish the impacts of different ads. To this end, we develop a Hierarchical Importance-aware Factorization Machine (HIFM), which provides an effective generic latent factor framework that incorporates importance weights and hierarchical …
On Finding The Point Where There Is No Return: Turning Point Mining On Game Data, Wei Gong, Ee Peng Lim, Feida Zhu, Achananuparp Palakorn, David Lo
On Finding The Point Where There Is No Return: Turning Point Mining On Game Data, Wei Gong, Ee Peng Lim, Feida Zhu, Achananuparp Palakorn, David Lo
David LO
Gaming expertise is usually accumulated through playing or watching many game instances, and identifying critical moments in these game instances called turning points. Turning point rules (shorten as TPRs) are game patterns that almost always lead to some irreversible outcomes. In this paper, we formulate the notion of irreversible outcome property which can be combined with pattern mining so as to automatically extract TPRs from any given game datasets. We specifically extend the well-known PrefixSpan sequence mining algorithm by incorporating the irreversible outcome property. To show the usefulness of TPRs, we apply them to Tetris, a popular game. We mine …
R-Energy For Evaluating Robustness Of Dynamic Networks, Ming Gao, Ee Peng Lim, David Lo
R-Energy For Evaluating Robustness Of Dynamic Networks, Ming Gao, Ee Peng Lim, David Lo
David LO
The robustness of a network is determined by how well its vertices are connected to one another so as to keep the network strong and sustainable. As the network evolves its robustness changes and may reveal events as well as periodic trend patterns that affect the interactions among users in the network. In this paper, we develop R-energy as a new measure of network robustness based on the spectral analysis of normalized Laplacian matrix. R-energy can cope with disconnected networks, and is efficient to compute with a time complexity of O (jV j + jEj) where V and E are …
F-Trail: Finding Patterns In Taxi Trajectories, Yasuko Matsubara, Evangelos Papalexakis, Lei Li, David Lo, Yasushi Sakurai, Christos Faloutsos
F-Trail: Finding Patterns In Taxi Trajectories, Yasuko Matsubara, Evangelos Papalexakis, Lei Li, David Lo, Yasushi Sakurai, Christos Faloutsos
David LO
Given a large number of taxi trajectories, we would like to find interesting and unexpected patterns from the data. How can we summarize the major trends, and how can we spot anomalies? The analysis of trajectories has been an issue of considerable interest with many applications such as tracking trails of migrating animals and predicting the path of hurricanes. Several recent works propose methods on clustering and indexing trajectories data. However, these approaches are not especially well suited to pattern discovery with respect to the dynamics of social and economic behavior. To further analyze a huge collection of taxi trajectories, …
An Empirical Study On Developer Interactions In Stackoverflow, Shaowei Wang, David Lo, Lingxiao Jiang
An Empirical Study On Developer Interactions In Stackoverflow, Shaowei Wang, David Lo, Lingxiao Jiang
David LO
No abstract provided.
Mining Indirect Antagonistic Communities From Social Interactions, Kuan Zhang, David Lo, Ee Peng Lim, Philips Kokoh Prasetyo
Mining Indirect Antagonistic Communities From Social Interactions, Kuan Zhang, David Lo, Ee Peng Lim, Philips Kokoh Prasetyo
David LO
Antagonistic communities refer to groups of people with opposite tastes, opinions, and factions within a community. Given a set of interactions among people in a community, we develop a novel pattern mining approach to mine a set of antagonistic communities. In particular, based on a set of user-specified thresholds, we extract a set of pairs of communities that behave in opposite ways with one another. We focus on extracting a compact lossless representation based on the concept of closed patterns to prevent exploding the number of mined antagonistic communities. We also present a variation of the algorithm using a divide …
Mining Top-K Large Structural Patterns In A Massive Network, Feida Zhu, Qiang Qu, David Lo, Xifeng Yan, Jiawei Han, Philip S. Yu
Mining Top-K Large Structural Patterns In A Massive Network, Feida Zhu, Qiang Qu, David Lo, Xifeng Yan, Jiawei Han, Philip S. Yu
David LO
With ever-growing popularity of social networks, web and bio-networks, mining large frequent patterns from a single huge network has become increasingly important. Yet the existing pattern mining methods cannot offer the efficiency desirable for large pattern discovery. We propose Spider- Mine, a novel algorithm to efficiently mine top-K largest frequent patterns from a single massive network with any user-specified probability of 1 − ϵ. Deviating from the existing edge-by-edge (i.e., incremental) pattern-growth framework, SpiderMine achieves its efficiency by unleashing the power of small patterns of a bounded diameter, which we call “spiders”. With the spider structure, our approach adopts a …
Mining Antagonistic Communities From Social Networks, Kuan Zhang, David Lo, Ee Peng Lim
Mining Antagonistic Communities From Social Networks, Kuan Zhang, David Lo, Ee Peng Lim
David LO
During social interactions in a community, there are often sub-communities that behave in opposite manner. These antagonistic sub-communities could represent groups of people with opposite tastes, factions within a community distrusting one another, etc. Taking as input a set of interactions within a community, we develop a novel pattern mining approach that extracts for a set of antagonistic sub-communities. In particular, based on a set of user specified thresholds, we extract a set of pairs of sub-communities that behave in opposite ways with one another. To prevent a blow up in these set of pairs, we focus on extracting a …
Efficient Topological Olap On Information Networks, Qiang Qu, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Hongyan Li
Efficient Topological Olap On Information Networks, Qiang Qu, Feida Zhu, Xifeng Yan, Jiawei Han, Philip Yu, Hongyan Li
David LO
We propose a framework for efficient OLAP on information networks with a focus on the most interesting kind, the topological OLAP (called “T-OLAP”), which incurs topological changes in the underlying networks. T-OLAP operations generate new networks from the original ones by rolling up a subset of nodes chosen by certain constraint criteria. The key challenge is to efficiently compute measures for the newly generated networks and handle user queries with varied constraints. Two effective computational techniques, T-Distributiveness and T-Monotonicity are proposed to achieve efficient query processing and cube materialization. We also provide a T-OLAP query processing framework into which these …
Mining Interesting Link Formation Rules In Social Networks, Cane Wing-Ki Leung, Ee Peng Lim, David Lo, Jianshu Weng
Mining Interesting Link Formation Rules In Social Networks, Cane Wing-Ki Leung, Ee Peng Lim, David Lo, Jianshu Weng
David LO
Link structures are important patterns one looks out for when modeling and analyzing social networks. In this paper, we propose the task of mining interesting Link Formation rules (LF-rules) containing link structures known as Link Formation patterns (LF-patterns). LF-patterns capture various dyadic and/or triadic structures among groups of nodes, while LF-rules capture the formation of a new link from a focal node to another node as a postcondition of existing connections between the two nodes. We devise a novel LF-rule mining algorithm, known as LFR-Miner, based on frequent subgraph mining for our task. In addition to using a support-confidence framework …
Mining And Ranking Generators Of Sequential Pattern, David Lo, Siau-Cheng Khoo, Jinyan Li
Mining And Ranking Generators Of Sequential Pattern, David Lo, Siau-Cheng Khoo, Jinyan Li
David LO
Sequential pattern mining ¯rst proposed by Agrawal and Srikant has received intensive research due to its wide range applicability in many real-life domains. Various improvements have been proposed which include mining a closed set of sequential patterns. Sequential patterns supported by the same sequences in the database can be considered as belonging to an equivalence class. Each equivalence class contains patterns partially-ordered by sub-sequence relationship and having the same support. Within an equivalence class, the set of maximal and minimal patterns are referred to as closed patterns and generators respectively. Generators used together with closed patterns can provide additional information …
Data Mining For Software Engineering, Tao Xie, Suresh Thummalapenta, David Lo, Chao Liu
Data Mining For Software Engineering, Tao Xie, Suresh Thummalapenta, David Lo, Chao Liu
David LO
To improve software productivity and quality, software engineers are increasingly applying data mining algorithms to various software engineering tasks. However, mining SE data poses several challenges. The authors present various algorithms to effectively mine sequences, graphs, and text from such data.