Inventory management refers to tracing inventory levels, orders and sales of a retailing business. In the current retailing market, a tremendous amount of data regarding stocked goods (items) in an inventory will be generated everyday. Due to the increasing volume of transaction data and the correlated relations of items, it is often a non-trivial task to efficiently and effectively manage stocked goods. In this demo, we present an intelligent system, called iMiner, to ease the management of enormous inventory data. We utilize distributed computing resources to process the huge volume of inventory data, and incorporate the latest advances of data mining technologies into the system to perform the tasks of inventory management, e.g., forecasting inventory, detecting abnormal items, and analyzing inventory aging. Since 2014, iMiner has been deployed as the major inventory management platform of ChangHong Electric Co., Ltd, one of the world's largest TV selling companies in China.

Advanced manufacturing such as aerospace, semi-conductor, and flat display device often involves complex production processes, and generates large volume of production data. In general, the production data comes from products with different levels of quality, assembly line with complex flows and equipments, and processing craft with massive controlling parameters. The scale and complexity of data is beyond the analytic power of traditional IT infrastructures. To achieve better manufacturing performance, it is imperative to explore the underlying dependencies of the production data and exploit analytic insights to improve the production process. However, few research and industrial efforts have been reported on providing manufacturers with integrated data analytical solutions to reveal potentials and optimize the production process from data-driven perspectives.

Ensemble clustering, as an important extension of the clustering problem, refers to the problem of combining different (input) clusterings of a given dataset to generate a final (consensus) clustering that is a better fit in some sense than existing clusterings. Over the past few years, many ensemble clustering approaches have been developed. However, most of them are designed for partitional clustering methods, and few research efforts have been reported for ensemble hierarchical clustering methods. In this article, a hierarchical ensemble clustering framework that can naturally combine both partitional clustering and hierarchical clustering results is proposed. In addition, a novel method for learning the ultra-metric distance from the aggregated distance matrices and generating final hierarchical clustering with enhanced cluster separation is developed based on the ultra-metric distance for hierarchical clustering. We study three important problems: dendrogram description, dendrogram combination, and dendrogram selection. We develop two approaches for dendrogram selection based on tree distances, and we investigate various dendrogram distances for representing dendrograms. We provide a systematic empirical study of the ensemble hierarchical clustering problem. Experimental results demonstrate the effectiveness of our proposed approaches.

The advent of Big Data era drives data analysts from different domains to use data mining techniques for data analysis. However, performing data analysis in a specific domain is not trivial; it often requires complex task configuration, onerous integration of algorithms, and efficient execution in distributed environments.Few efforts have been paid on developing effective tools to facilitate data analysts in conducting complex data analysis tasks.

Techniques to efficiently discover, collect, organize, search, and disseminate real-time disaster information have become national priorities for efficient crisis management and disaster recovery tasks. We have developed techniques to facilitate information sharing and collaboration between both private and public sector participants for major disaster recovery planning and management. We have designed and implemented two parallel systems: a web-based prototype of a Business Continuity Information Network system and an All-Hazard Disaster Situation Browser system that run on mobile devices. Data mining and information retrieval techniques help impacted communities better understand the current disaster situation and how the community is recovering. Specifically, information extraction integrates the input data from different sources; report summarization techniques generate brief reviews from a large collection of reports at different granularities; probabilistic models support dynamically generating query forms and information dashboard based on user feedback; and community generation and user recommendation techniques are adapted to help users identify potential contacts for report sharing and community organization. User studies with more than 200 participants from EOC personnel and companies demonstrate that our systems are very useful to gain insights about the disaster situation and for making decisions.

Semi-supervised clustering (i.e., clustering with knowledge-based constraints) has emerged as an important variant of the traditional clustering paradigms. However, most existing semi-supervised clustering algorithms are designed for partitional clustering methods and few research efforts have been reported on semi-supervised hierarchical clustering methods. In addition, current semi-supervised clustering methods have been focused on the use of background information in the form of instance level must-link and cannot-link constraints, which are not suitable for hierarchical clustering where data objects are linked over different hierarchy levels. In this paper, we propose a novel semi-supervised hierarchical clustering framework based on ultra-metric dendrogram distance. The proposed framework is able to incorporate triple-wise relative constraints. We establish the connection between hierarchical clustering and ultra-metric transformation of dissimilarity matrix and propose two techniques (the constrained optimization technique and the transitive dissimilarity based technique) for semi-supervised hierarchical clustering. Experimental results demonstrate the effectiveness and the efficiency of our proposed methods.

The improvement of Crisis Management and Disaster Recovery techniques are national priorities in the wake of man-made and nature inflicted calamities of the last decade. Our prior work has demonstrated that the efficiency of sharing and managing information plays an important role in business recovery efforts after disaster event. With the proliferation of smart phones and wireless tablets, professionals who have an operational responsibility in disaster situations are relying on such devices to maintain communication. Further, with the rise of social media, technology savvy consumers are also using these devices extensively for situational updates. In this paper, we address several critical tasks which can facilitate information sharing and collaboration between both private and public sector participants for major disaster recovery planning and management. We design and implement an All-Hazard Disaster Situation Browser (ADSB) system that runs on Apple's mobile operating system (iOS) and iPhone and iPad mobile devices. Our proposed techniques create a collaborative solution on a mobile platform using advanced data mining and information retrieval techniques for disaster preparedness and recovery that helps impacted communities better understand the current disaster situation and how the community is recovering. Specifically, hierarchical summarization techniques are used to generate brief reviews from a large collection of reports at different granularities; probabilistic models are proposed to dynamically generate query forms based on user's feedback; and recommendation techniques are adapted to help users identify potential contacts for report sharing and community organization. Furthermore, the developed techniques are designed to be all-hazard capable so that they can be used in earthquake, terrorism, or other unanticipated disaster situations.

In this paper, we initially provide an experimental study on the evolution of user interests in real-world news recommender systems, and then propose a novel recommendation approach, in which the long-term and short-term reading preferences of users are seamlessly integrated when recommending news items. Given a hierarchy of newly-published news articles, news groups that the user might prefer are differentiated using the long-term profile, and then in each selected news group, a list of news items are chosen based on the short-term user profile. Extensive empirical experiments on a collection of news articles obtained from various popular news websites demonstrate the efficacy of our method.

Ensemble clustering has emerged as an important elaboration of the classical clustering problems. Ensemble clustering refers to the situation in which a number of different (input) clusterings have been obtained for a particular dataset and it is desired to find a single (consensus) clustering which is a better fit in some sense than the existing clusterings. Many approaches have been developed to solve ensemble clustering problems over the last few years. However, most of these ensemble techniques are designed for partitional clustering methods. Few research efforts have been reported for ensemble hierarchical clustering methods. In this paper, we propose a hierarchical ensemble clustering framework which can naturally combine both partitional clustering and hierarchical clustering results. We notice the importance of ultra-metric distance for hierarchical clustering and propose a novel method for learning the ultra-metric distance from the aggregated distance matrices and generating final hierarchical clustering with enhanced cluster separation. Experimental results demonstrate the effectiveness of our proposed approaches.

Crisis Management and Disaster Recovery have gained immense importance in the wake of recent man and nature inflicted calamities. A critical problem in a crisis situation is how to efficiently discover, collect, organize, search and disseminate real-time disaster information. In this paper, we address several key problems which inhibit better information sharing and collaboration between both private and public sector participants for disaster management and recovery. We design and implement a web based prototype implementation of a Business Continuity Information Network (BCIN) system utilizing the latest advances in data mining technologies to create a user-friendly, Internet-based, information-rich service and acting as a vital part of a company's business continuity process. Specifically, information extraction is used to integrate the input data from different sources; the content recommendation engine and the report summarization module provide users personalized and brief views of the disaster information; the community generation module develops spatial clustering techniques to help users build dynamic community in disasters. Currently, BCIN has been exercised at Miami-Dade County Emergency Management.

In this poster, we develop an evolutionary document summarization system for discovering the changes and differences in each phase of a disaster evolution. Given a collection of document streams describing an event, our system generates a short summary delivering the main development theme of the event by extracting the most representative and discriminative sentences at each phase. Experimental results on the collection of press releases for Hurricane Wilma in 2005 demonstrate the efficacy of our proposal.