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While geographical metadata referring to the originating locations of tweets provides valuable information to perform effective spatial analysis in social networks, scarcity of such geotagged tweets imposes limitations on their usability. In this work, we propose a content-based location prediction method for tweets by analyzing the geographical distribution of tweet texts using Kernel Density Estimation (KDE). The primary novelty of our work is to determine different settings of kernel functions for every term in tweets based on the location indicativeness of these terms. Our proposed method, which we call locality-adapted KDE, uses information-theoretic metrics and does not require any parameter tuning for these settings. As a further enhancement on the term-level distribution model, we describe an analysis of spatial point patterns in tweet texts in order to identify bigrams that exhibit significant deviation from the underlying unigram patterns. We present an expansion of feature space using the selected bigrams and show that it eventually yields further improvement in prediction accuracy of our locality-adapted KDE. We demonstrate that our expansion results in a limited increase in the size of feature space and it does not hinder online localization of tweets. The methods we propose rely purely on statistical approaches without requiring any language-specific setting. Experiments conducted on three tweet sets from different countries show that our proposed solution outperforms existing state-of-the-art techniques, yielding significantly more accurate predictions.

The set of closed high-utility itemsets (CHUIs) concisely represents the exact utility of all itemsets. Yet, it can be several orders of magnitude smaller than the set of all high-utility itemsets. Existing CHUI mining algorithms assume that databases are static, making them very expensive in the case of incremental data, since the whole dataset has to be processed for each batch of new transactions. To address this challenge, this paper presents the first approach, called IncCHUI, that mines CHUIs efficiently from incremental databases. In order to achieve this, we propose an incremental utility-list structure, which is built and updated with only one database scan. Further, we apply effective pruning strategies to fast construct incremental utility-lists and eliminate candidates that are not updated. Finally, we suggest an efficient hash-based approach to update or insert new closed sets that are found. Our extensive experimental evaluation on both real-life and synthetic databases shows the efficiency, as well as the feasibility of our approach. It significantly outperforms previously proposed methods that are mainly run in batch mode in terms of speed, and it is scalable with respect to the number of transactions.

This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction database based on clustering techniques. The set of transactions is first clustered, such that highly correlated transactions are grouped together. Next, we derive the relevant patterns by applying a pattern mining algorithm to each cluster. We present two different pattern mining algorithms, one applying an approximation-based strategy and another based on an exact strategy. The approximation-based strategy takes into account only the clusters, whereas the exact strategy takes into account both clusters and shared items between clusters. To boost the performance of the CBPM, a GPU-based implementation is investigated. To evaluate the CBPM framework, we perform extensive experiments on several pattern mining problems. The results from the experimental evaluation show that the CBPM provides a reduction in both the runtime and memory usage. Also, CBPM based on the approximate strategy provides good accuracy, demonstrating its effectiveness and feasibility. Our GPU implementation achieves significant speedup of up to 552× on a single GPU using big transaction databases. © ACM, 2021. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published here, https://doi.org/10.1145/3340531.3412097

Dense subtensor detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Existing algorithms are generally efficient for dense subtensor detection and could perform well in many applications. However, the main drawback of most of these algorithms is that they can estimate only one subtensor at a time, with a low guarantee on the subtensor’s density. While some methods can, on the other hand, estimate multiple subtensors, they can give a guarantee on the density with respect to the input tensor for the first estimated subsensor only. We address these drawbacks by providing both theoretical and practical solution for estimating multiple dense subtensors in tensor data. In particular, we guarantee and prove a higher bound of the lower-bound density of the estimated subtensors. We also propose a novel approach to show that there are multiple dense subtensors with a guarantee on its density that is greater than the lower bound used in the state-of-the-art algorithms. We evaluate our approach with extensive experiments on several real- world datasets, which demonstrates its efficiency and feasibility. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

By "checking into'' various points-of-interest (POIs), users create a rich source of location-based social network data that can be used in expressive spatio-social queries. This paper studies the use of popularity as a means to diversify results of top-k nearby POI queries. In contrast to previous work, we evaluate social diversity as a group-based, rather than individual POI, metric. Algorithmically, evaluating this set-based notion of diversity is challenging, yet we present several effective algorithms based on (integer) linear programming, a greedy framework, and r-tree distance browsing. Experiments show scalability and interactive response times for up to 100 million unique check-ins across 25000 POIs. © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published here, https://doi.org/10.1145/3340531.3412097