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Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful...
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...
We present an accelerated algorithm for hierarchical density based clustering. Our new algorithm improves upon HDBSCAN*, which itself provided a significant qualitative improvement over the popular DBSCAN algorithm. The accelerated HDBSCAN* algorithm provides comparable performance to DBSCAN, while supporting variable density clusters, and eliminating the need for the difficult to tune distance scale...
Anecdotal evidence suggests that the variety of Big data is one of the most challenging problems in Computer Science research today [Stonebraker, 2012], [Ou et al., 2017], [Guo et al., 2016], [Bai et al., 2016]. First, Big data comes at us from a myriad of data sources, hence its shape and flavor differ. Second, hundreds of data management systems which work with Big data support different APIs and...
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...
Given a beginning and ending document, automated storytelling attempts to fill in intermediary documents to form a coherent story. This is a common problem for analysts; they often have two snippets of information and want to find the other pieces that relate them. Evaluation of the quality of the created stories is difficult and has routinely involved human judgment. This work extends the state of...
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...
We treat failure prediction in a supervised learning framework using a convolutional neural network (CNN). Due to the nature of the problem, learning a CNN model on this kind of dataset is generally associated with three primary problems: 1) negative samples (indicating a healthy system) outnumber positives (indicating system failures) by a great margin; 2) implementation design often requires chopping...
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...
Residential Demand Response has emerged as an instrument of the modern smart grid to alleviate supply and demand imbalances of electricity. Utilizing their flexibility of electricity demand, residential households are offered monetary incentives to temporarily reduce energy consumption during times when the grid is strained due to a supply shortage. In this paper, we estimate the magnitude of reductions...
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