Abstract

Networks have become ubiquitous in many real world appli-cations and to cluster similar networks is an important problem. There are various properties of graphs such as clustering coefficient (CC), den-sity, arboricity, etc. We introduce a measure, Clique Conversion Coeffi-cient (CCC), which captures the clique forming tendency of nodes in anundirected graph. CCC could either be used as a weighted average ofthe values in a vector or as the vector itself. Our experiments show thatCCC provides additional information about a graph in comparison torelated measures like CC and density. We cluster the real world graphsusing a combination of the features CCC, CC, and density and show thatwithout CCC as one of the features, graphs with similar clique formingtendencies are not clustered together. The clustering with the use ofCCC would have applications in the areas of Social Network Analysis,Protein-Protein Interaction Analysis, etc., where cliques have an impor-tant role. We perform the clustering of ego networks of the YOUTUBEnetwork using values in CCC vector as features. The quality of the clus-tering is analyzed by contrasting the frequent subgraphs in each cluster.The results highlight the utility of CCC in clustering subgraphs of alarge graph

Abstract

Time and frequency are two important dimensions to determine the interestingness of a pattern in a database. Periodic-frequent patterns are an important class of regularities that exist in a database with respect to these two dimensions. Current studies on periodic-frequent pattern mining have focused on discovering full periodic-frequent patterns, i.e., finding all frequent patterns that have exhibited complete cyclic repetitions in a database. However, partial periodic-frequent patterns are more common due to the imperfect nature of real-world. This paper proposes a flexible and generic model to find partial periodic-frequent patterns. A new interesting measure, periodic-ratio, has been introduced to determine the periodic interestingness of a frequent pattern by taking into account its proportion of cyclic repetitions in a database. The proposed patterns do not satisfy the anti-monotonic property. A novel pruning technique has been introduced to reduce the search space effectively. A pattern-growth algorithm to find all partial periodic-frequent patterns has also been presented in this paper. Experimental results demonstrate that the proposed model can discover useful information, and the algorithm is efficient.

Abstract

We propose an interactive visual analytics system for exploring spatio-temporal data in VAST 2017 Mini-Challenge-1. As part of this challenge, we are expected to determine repeating, seasonal and unusual cars' movements in Lekagul park dataset. We use varied visualizations such as heatmap, sequential sunburst and line plots. Further, we have developed a web deployable ad-hoc system for displaying spatio-temporal information on geographical map

Abstract

A temporal database is a collection of transactions, ordered by their timestamps. Discovering periodic patterns in temporal databases has numerous applications. However, to the best of our knowledge, no work has considered mining periodic patterns in temporal databases where items have dissimilar support and periodicity, despite that this type of data is very common in real-life. Discovering periodic patterns in such non-uniform temporal databases is challenging. It requires defining (i) an appropriate measure to assess the periodic interestingness of patterns, and (ii) a method to efficiently find all periodic patterns. While a pattern-growth approach can be employed for the second sub-task, the first sub-task has to the best of our knowledge not been addressed. Moreover, how these two tasks are combined has significant implications. In this paper, we address this challenge. We introduce a model to assess the periodic interestingness of patterns in databases having a non-uniform item distribution, which considers that periodic patterns may have different period and minimum number of cyclic repetitions. Moreover, the paper introduces a pattern-growth algorithm to efficiently discover all periodic patterns. Experimental results demonstrate that the proposed algorithm is efficient and the proposed model may be utilized to find prior knowledge about event keywords and their associations in Twitter data.

Abstract

Community-driven Knowledge Sharing (KS) plat-forms have gained immense popularity in recent years among the Internet users to seek, learn & share information and expertise.These platforms encourage rich content by recognizing users’ contributions; measured as the reputation on platforms. Thus,peers on the platform dissuade other users to post low quality content by closing, disliking or not answering their questions.The aggressive attitude of the community leads to a negative impact on the users’ experience (especially for newbies) and the users tend to lose interest in the platform. Since the quality management on KS platforms is a necessity, we aim to emphasize the need of a mechanism to help users in improving their questions. To study this, we perform experiments on the Stack Overflow (SO) platform, however, the study can be adapted for other KS platforms.The SO community aggressively mark questions, even with slightest deformity, as closed. The non-triviality of reopening process and the rigorous review mechanism can be handled only by proper editing of the closed questions. Thus we present first-of-a-kind study, to assist the SO platform users, in the reopening process of their closed questions. We build predictive models to suggest the users that if their edited version of closed question will lead to a successful reopen or not. This can assist users at large by retracting them from entering the review process with improper edits. To learn these models effectively, we consider the user categories on the platform, based on their reputation: established& non-established. In addition to being the major contributor to closed questions, the non-established users have lower odds of getting their closed questions reopened than established users.Thus, we leverage the better editing skills of established users to learn question edit models by employing their reopened closed questions. We present the results of predictive model and suggest insights on the useful edits in the reopening process on the SO platform

Abstract

Periodic Frequent patterns (PFPs) are an important class of regularities that exist in a transactional database. In the literature,pattern growth-based approaches to mine PFPs have be proposed by considering a single machine. In this paper, we propose a Map-Reduce framework to mine PFPs by considering multiple machines. We have proposed a parallel algorithm by including the step of distributing transactional identifiers among the machines. Further, the notion of partition summary has been proposed to reduce the amount of data shuffled among the machines. Experiments on Apache Spark’s distributed environment show that the proposed approach speeds up with the increase in number of machines and the notion of partition summary significantly reduces the amount of data shuffled among the machines.

Abstract

Utility mining has been emerging as an important area in data mining. While existing works on utility mining have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly assume that each item occupies only one slot. However, in many real-world scenarios, the number of slots consumed by different items typically varies. Hence, this pa-per considers that a given item may physically occupy any fixed (integer) number of slots. Thus, we address the problem of efficiently determining the top-utility itemsets when a given number of slots is specified as input. The key contributions of our work are three-fold. First, we present an efficient framework to determine the top-utility itemsets for different user-specified number of slots that need to be filled. Second, we propose a novel flexible and efficient index, designated as the STUI index, for facilitating quick retrieval of the top-utility itemsets for a given number of slots. Third, we conducted an extensive performance evaluation using real datasets to demonstrate the overall effectiveness of the proposed indexing scheme in terms of execution time and utility (net revenue) as compared to a recent existing scheme.

Abstract

Extracting structured information from unstructured text is a critical problem. Over the past few years, various clustering algorithms have been proposed to solve this problem. In addition, various algorithms based on probabilistic topic models have been developed to find the hidden thematic structure from various corpora (i.e publications, blog setc). Both types of algorithms have been transferred to the domain of scientific literature to extract structured information to solve problems like data exploration, expert detection etc. In order to remain domain-agnostic, these algorithms do not exploit the structure present in a scientific publication. Majority of researchers interpret a scientific publication as research conducted to report progress in solving some research problems.Following this interpretation, in this paper we present a different outlook to the same problem by modelling scientific publications around research problems. By associating a scientific publication with a research problem, exploring the scientific literature becomes more intuitive. In this paper, we propose an unsupervised framework to mine research problems from titles and abstracts of scientific literature. Our framework uses weighted frequent phrase mining to generate phrases and filters them to obtain high-quality phrases.These high-quality phrases are then used to segment the scientific publication into meaningful semantic units. After segmenting publications, we apply a number of heuristics to score the phrases and sentences to identify the research problems. In a post-processing step we use a neighborhood based algorithm to merge different representations of the same problems. Experiments conducted on parts of DBLP dataset show promising results.

Abstract

Cricket is one of the most popular team sports in the world.Players have multiple roles in a game of cricket, predominantly as bats-men and bowlers. Over the generations, statistics such as batting and bowling averages, and strike and economy rates have been used to judge the performance of individual players. These measures, however, do not take into consideration the context of the game in which a player per-formed. Furthermore, these types of statistics are incapable of comparing the performance of players across different roles. In this paper, we present an approach to quantitatively assess the performances of individual players in single match of One Day International (ODI) cricket. For this, we have developed a new measure, called the Work Index, which represents the amount of work that is yet to be done by a team to achieve its tar-get. Our approach incorporates game situations and the team strength sto measure the player contributions. This not only helps us in evaluating the individual performances, but also enables us to compare players within and across various roles on a common scale. Using the player performances in a match, we predict the player of the match award for the ODI matches played between 2006 and 2016. We have achieved an accuracy of 86.80% for the top-3 positions, which is superior to base linemodels and previous works, to the best of our knowledge.

Abstract

We present a regression-based feature learning algorithm that generates new features from a set of available features (raw data points). Being data-driven, it requires no domain knowledge and is hence generic. Such a representation is learnt by mining pairwise feature associations, identifying the linear or non-linear relationship between each pair, applying regression and selecting those relationships that are stable.Our experimental evaluation on 20 datasets taken from UC Irvine and Gene Expression, across different domains, provides evidence that the features learnt through our model can improve the overall prediction accuracy, substantially, over the original feature space across 8 different classifiers without any domain knowledge.