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With the spreading prevalence of Big Data, many advances have recently been made in this field. Frameworks such as Apache Hadoop and Apache Spark have gained a lot of traction over the past decades and have become massively popular, especially in industries. It is becoming increasingly evident that effective big data analysis is key to solving artificial intelligence problems. Thus, a multi-algorithm...
Due to the increasing popularity of cooking-recipe sharing sites and the success of complex network science, attention has recently been devoted to developing an effective networkbased method of analyzing the characteristics of ingredient combinations used in recipes. Unlike previous approaches dealing with static properties, we aim at analyzing the dynamical changes in ingredient pairs jointly used...
Clustering is an important branch in the field of data mining as well as statistical analysis and is widely used in exploratory analysis. Many algorithms exist for clustering in the Euclidean space. However, time series clustering introduces new problems, such as inadequate distance measure, inaccurate cluster center description, lack of efficient and accurate clustering techniques. When dealing with...
Action Rules are vital data mining method for gaining actionable knowledge from the datasets. Meta actions are the sub-actions to the Action Rules, which intends to change the attribute value of an object, under consideration, to attain the desirable value. The essence of this paper to propose a new optimized and more promising system, in terms of speed and efficiency, for generating meta-actions...
Highly imbalanced datasets continue to be a challenge in many data mining applications. It is surprising that state-of-the-art techniques countering class imbalances are usually very computationally expensive and therefore unscalable. Most research effort has been directed into enhancing those techniques, e.g., by focusing on borderline examples or combining multiple techniques. This is usually accompanied...
Action Rules or Actionable patterns is a type of rule-based approach in data mining that recommends to a user specific actions, in order to achieve a desired result or goal. The amount of data in the world is growing at an exponential rate, doubling almost every two years. Distributed computing platforms like Hadoop and Spark, have eased the computation of this high velocity data. Leveraging these...
There has been a surge in research interest in learning feature representation of networks in recent times. Researchers, motivated by the recent successes of embeddings in natural language processing and advances in deep learning, have explored various means for network embedding. Network embedding is useful as it can exploit off-the-shelf machine learning algorithms for network mining tasks like...
Finding the best candidates to match a set of job requirements can be viewed as both an art and a science. In this paper, we conduct an empirical study using actual job candidates and job applicants. We compare the ranked lists generated by executive recruiting experts with the list generated by three search strategies: one using crowdworkers in a gamified environment, a second using information retrieval-based...
According to a report online [34], more than 200 million unique users search for jobs online every month. This incredibly large and fast growing demand has enticed software giants such as Google and Facebook to enter this space, which was previously dominated by companies such as LinkedIn, Indeed, Dice and CareerBuilder. Recently, Google released their “AIpowered Jobs Search Engine”, “Google For Jobs”...
Continuous training is crucial for creating and maintaining the right skill-profile for the industrial organization's workforce. There is a tremendous variety in the available trainings within an organization: technical, project management, quality, leadership, domain-specific, soft-skills etc. Hence it is important to assist the employee in choosing the best trainings, which perfectly suits her background,...
Spatiotemporal event sequences (STESs) are the ordered series of event types whose evolving region-based instances frequently follow each other in time and are located closeby. Previous studies on STES mining require significance and prevalence thresholds for the discovery, which is usually unknown to domain experts. As the quality of the discovered STESs is of great importance to the domain experts...
Given a database of spatial trajectories reporting the movement of a set of objects in a time frame, the problem is to discover the groups of objects that stay in close proximity within a geographical area for a significant time. To deal with the problem, techniques for the discovery of collective patterns, e.g. the meeting pattern, have been proposed. Such techniques, however, impose stringent constraints...
We present a novel and configurable synthetic data generator for evolving region trajectories that emulates certain characteristics of a given input dataset, such as the spatial position, velocity, lifespan, and geometry shape and size. This tool aims to facilitate faster prototyping and evaluation of new spatiotemporal data mining algorithms that operate on a specific type of trajectory data, of...
Extracting stop purpose information from raw GPS data is a crucial task in most location-aware applications. With the continuous growth of GPS data collected from mobile devices, this task is becoming more and more interesting; a lot of recent research has focused on pedestrians (mobile phones) data, while the commercial vehicles sector is almost unexplored. In this paper we target the problem of...
Certain environmental processes, while influential, are inherently difficult to quantify and detect using traditional time series analyses, particularly among variables with different seasonal progressions. Disturbances that only manifest in part of a season (e.g., spring defoliation) or subtle climate shifts can pose detection challenges when they occur in the presence of other variability. Increasing...
Caregiving is the act of providing assistance to an individual unable to perfom some daily living activities. Caregiving can be either paid or unpaid. An informal caregiver is an unpaid caregiver to an older, sick, or disabled family member or friend on a daily basis. Informal caregiving is associated with increased physical, mental, and emotional stressors contributing to poor health outcomes, caregiver...
An individual's personality determines the probable repertoire of their reactions to a particular situation. A social robot is much more effective if it is able to learn and so take into account the properties of the humans around it, including personalities. We investigate how well personality can be estimated based on modest amounts of speech or writing, which a social robot might (over)hear. Such...
Automatic sentiment classification is becoming a popular and effective way to help online users or companies process and make sense of customer reviews. In this article, a learning-based method for classification of online reviews that achieves better classification accuracy is obtained by (a) combining valence shifters and opinion words into bigrams for use as features in an ordinal margin classifier...
Social media serves as a unified platform for users to express their thoughts on subjects ranging from their daily lives to their opinion on consumer brands and products. These users wield an enormous influence in shaping the opinions of other consumers and influence brand perception, brand loyalty and brand advocacy. In this paper, we analyze the opinion of 19M Twitter users towards 62 popular industries,...
We present the results of an experiment to assess the validity of prior polarities available in sentiment lexicons. We designed a ranking task that was elicited through pairwise comparisons and compared the results to those predicted by two popular sentiment lexicons. We find that the experiment results show a moderate level of agreement between the lexicons and human judgments.
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