2017 IEEE International Conference on Data Mining Workshops (ICDMW)

18-21 Nov. 2017

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  • [Front cover]

    Publication Year: 2017, Page(s): c1
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  • [Title page i]

    Publication Year: 2017, Page(s): i
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  • [Title page iii]

    Publication Year: 2017, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2017, Page(s): iv
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  • Table of contents

    Publication Year: 2017, Page(s):v - xvi
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  • Message from the Conference Chairs

    Publication Year: 2017, Page(s):xvii - xviii
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  • Message from the Workshop Co-Chairs

    Publication Year: 2017, Page(s):xix - xx
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  • Organizing Committee

    Publication Year: 2017, Page(s):xxi - xxii
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  • Intent-Aware Contextual Recommendation System

    Publication Year: 2017, Page(s):1 - 8
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (272 KB) | HTML iconHTML

    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 u... View full abstract»

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  • A Big Data Analysis Framework Using Apache Spark and Deep Learning

    Publication Year: 2017, Page(s):9 - 16
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (554 KB) | HTML iconHTML

    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-algori... View full abstract»

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  • Analyzing Dynamical Activities of Co-occurrence Patterns for Cooking Ingredients

    Publication Year: 2017, Page(s):17 - 24
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (341 KB) | HTML iconHTML

    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 network-based 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 u... View full abstract»

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  • Distance and Density Clustering for Time Series Data

    Publication Year: 2017, Page(s):25 - 32
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (607 KB) | HTML iconHTML

    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 w... View full abstract»

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  • Accelerated Hierarchical Density Based Clustering

    Publication Year: 2017, Page(s):33 - 42
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (792 KB) | HTML iconHTML

    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 sc... View full abstract»

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  • Hybrid.poly: An Interactive Large-Scale In-memory Analytical Polystore

    Publication Year: 2017, Page(s):43 - 50
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (437 KB) | HTML iconHTML

    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 an... View full abstract»

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  • Action Rules for Sentiment Analysis on Twitter Data Using Spark

    Publication Year: 2017, Page(s):51 - 60
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (678 KB) | HTML iconHTML

    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 ... View full abstract»

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  • Automated Storytelling Evaluation and Story Chain Generation

    Publication Year: 2017, Page(s):61 - 68
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (938 KB) | HTML iconHTML

    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 o... View full abstract»

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  • Dealing with Class Imbalance the Scalable Way: Evaluation of Various Techniques Based on Classification Grade and Computational Complexity

    Publication Year: 2017, Page(s):69 - 78
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (337 KB) | HTML iconHTML

    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 ac... View full abstract»

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  • Failure Prediction with Adaptive Multi-scale Sampling and Activation Pattern Regularization

    Publication Year: 2017, Page(s):79 - 86
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (572 KB) | HTML iconHTML

    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 cho... View full abstract»

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  • Discovery of Action Rules at Lowest Cost in Spark

    Publication Year: 2017, Page(s):87 - 94
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (337 KB) | HTML iconHTML

    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... View full abstract»

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  • Estimating Treatment Effects of a Residential Demand Response Program Using Non-experimental Data

    Publication Year: 2017, Page(s):95 - 102
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (14816 KB) | HTML iconHTML

    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 redu... View full abstract»

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  • Deep and Confident Prediction for Time Series at Uber

    Publication Year: 2017, Page(s):103 - 110
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (684 KB) | HTML iconHTML

    Reliable uncertainty estimation for time series prediction is critical in many fields, including physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for robust prediction of number of trips during special events, driver incentive allocation, as well as real-time anomaly detection across millions of metrics. Classical time series models are often used in conj... View full abstract»

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  • Distributed Representations of Subgraphs

    Publication Year: 2017, Page(s):111 - 117
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1394 KB) | HTML iconHTML

    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 ... View full abstract»

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  • Network Model Selection for Task-Focused Attributed Network Inference

    Publication Year: 2017, Page(s):118 - 125
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (419 KB) | HTML iconHTML

    Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model sel... View full abstract»

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  • Mining Patterns of Cyberbullying on Twitter

    Publication Year: 2017, Page(s):126 - 133
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (673 KB) | HTML iconHTML

    Cyberbullying refers to the use of text, images, audio and video to harass or harm individuals or groups on a repetitive and non-stop basis in online social networks. The phenomenon has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyberbullying instances to mitigate the consequences. We perform a detailed analysis of a large-scale real-... View full abstract»

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  • Principled Multilayer Network Embedding

    Publication Year: 2017, Page(s):134 - 141
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2500 KB) | HTML iconHTML

    Multilayer network analysis has become a vital tool for understanding different relationships and their interactions in a complex system, where each layer in a multilayer network depicts the topological structure of a group of nodes corresponding to a particular relationship. The interactions among different layers imply how the interplay of different relations on the topology of each layer. For a... View full abstract»

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