A framework for clustering and adaptive topic tracking on evolving text and social media data streams.

Recent advances and widespread usage of online web services and social media platforms, coupled with ubiquitous low cost devices, mobile technologies, and increasing capacity of lower cost storage, has led to a proliferation of Big data, ranging from, news, e-commerce clickstreams, and online business transactions to continuous event logs and social media expressions. These large amounts of online data, often referred to as data streams, because they get generated at extremely high throughputs or velocity, can make conventional and classical data analytics methodologies obsolete. For these reasons, the issues of management and analysis of data streams have been researched extensively in recent years. The special case of social media Big Data brings additional challenges, particularly because of the unstructured nature of the data, specifically free text. One classical approach to mine text data has been Topic Modeling. Topic Models are statistical models that can be used for discovering the abstract ``topics\u27\u27 that may occur in a corpus of documents. Topic models have emerged as a powerful technique in machine learning and data science, providing a great balance between simplicity and complexity. They also provide sophisticated insight without the need for real natural language understanding. However they have not been designed to cope with the type of text data that is abundant on social media platforms, but rather for traditional medium size corpora consisting of longer documents, adhering to a specific language and typically spanning a stable set of topics. Unlike traditional document corpora, social media messages tend to be very short, sparse, noisy, and do not adhere to a standard vocabulary, linguistic patterns, or stable topic distributions. They are also generated at high velocity that impose high demands on topic modeling; and their evolving or dynamic nature, makes any set of results from topic modeling quickly become stale in the face of changes in the textual content and topics discussed within social media streams. In this dissertation, we propose an integrated topic modeling framework built on top of an existing stream-clustering framework called Stream-Dashboard, which can extract, isolate, and track topics over any given time period. In this new framework, Stream Dashboard first clusters the data stream points into homogeneous groups. Then data from each group is ushered to the topic modeling framework which extracts finer topics from the group. The proposed framework tracks the evolution of the clusters over time to detect milestones corresponding to changes in topic evolution, and to trigger an adaptation of the learned groups and topics at each milestone. The proposed approach to topic modeling is different from a generic Topic Modeling approach because it works in a compartmentalized fashion, where the input document stream is split into distinct compartments, and Topic Modeling is applied on each compartment separately. Furthermore, we propose extensions to existing topic modeling and stream clustering methods, including: an adaptive query reformulation approach to help focus on the topic discovery with time; a topic modeling extension with adaptive hyper-parameter and with infinite vocabulary; an adaptive stream clustering algorithm incorporating the automated estimation of dynamic, cluster-specific temporal scales for adaptive forgetting to help facilitate clustering in a fast evolving data stream. Our experimental results show that the proposed adaptive forgetting clustering algorithm can mine better quality clusters; that our proposed compartmentalized framework is able to mine topics of better quality compared to competitive baselines; and that the proposed framework can automatically adapt to focus on changing topics using the proposed query reformulation strategy

Stream-dashboard : a big data stream clustering framework with applications to social media streams.

Data mining is concerned with detecting patterns of data in raw datasets, which are then used to unearth knowledge that might not have been discovered using conventional querying or statistical methods. This discovered knowledge has been used to empower decision makers in countless applications spanning across many multi-disciplinary areas including business, education, astronomy, security and Information Retrieval to name a few. Many applications generate massive amounts of data continuously and at an increasing rate. This is the case for user activity over social networks such as Facebook and Twitter. This flow of data has been termed, appropriately, a Data Stream, and it introduced a set of new challenges to discover its evolving patterns using data mining techniques. Data stream clustering is concerned with detecting evolving patterns in a data stream using only the similarities between the data points as they arrive without the use of any external information (i.e. unsupervised learning). In this dissertation, we propose a complete and generic framework to simultaneously mine, track and validate clusters in a big data stream (Stream-Dashboard). The proposed framework consists of three main components: an online data stream clustering algorithm, a component for tracking and validation of pattern behavior using regression analysis, and a component that uses the behavioral information about the detected patterns to improve the quality of the clustering algorithm. As a first component, we propose RINO-Streams, an online clustering algorithm that incrementally updates the clustering model using robust statistics and incremental optimization. The second component is a methodology that we call TRACER, which continuously performs a set of statistical tests using regression analysis to track the evolution of the detected clusters, their characteristics and quality metrics. For the last component, we propose a method to build some behavioral profiles for the clustering model over time, that can be used to improve the performance of the online clustering algorithm, such as adapting the initial values of the input parameters. The performance and effectiveness of the proposed framework were validated using extensive experiments, and its use was demonstrated on a challenging real word application, specifically unsupervised mining of evolving cluster stories in one pass from the Twitter social media streams