waashk/dblp huggingface dataset:
support feature machine for classification of abnormal brain activity. in this study, a novel multidimensional time series classification technique, namely support feature machine (sfm), is proposed. sfm is inspired by the optimization model of support vector machine and the nearest neighbor rule to incorporate both spatial and temporal of the multi-dimensional time series data. this paper also describes an application of sfm for detecting abnormal brain activity. epilepsy is a case in point in this study. in epilepsy studies, electroencephalograms (eegs), acquired in multidimensional time series format, have been traditionally used as a gold-standard tool for capturing the electrical changes in the brain. from multi-dimensional eeg time series data, sfm was used to identify seizure pre-cursors and detect seizure susceptibility (pre-seizure) periods. the empirical results showed that sfm achieved over 80% correct classification of per-seizure eeg on average in 10 patients using 5-fold cross validation. the proposed optimization model of sfm is very compact and scalable, and can be implemented as an online algorithm. the outcome of this study suggests that it is possible to construct a computerized algorithm used to detect seizure pre-cursors and warn of impending seizures through eeg classification.
density-based clustering for real-time stream data. existing data-stream clustering algorithms such as clustream arebased on k-means. these clustering algorithms are incompetent tofind clusters of arbitrary shapes and cannot handle outliers. further, they require the knowledge of k and user-specified time window. to address these issues, this paper proposes d-stream, a framework for clustering stream data using adensity-based approach. the algorithm uses an online component which maps each input data record into a grid and an offline component which computes the grid density and clusters the grids based on the density. the algorithm adopts a density decaying technique to capture the dynamic changes of a data stream. exploiting the intricate relationships between the decay factor, data density and cluster structure, our algorithm can efficiently and effectively generate and adjust the clusters in real time. further, a theoretically sound technique is developed to detect and remove sporadic grids mapped to by outliers in order to dramatically improve the space and time efficiency of the system. the technique makes high-speed data stream clustering feasible without degrading the clustering quality. the experimental results show that our algorithm has superior quality and efficiency, can find clusters of arbitrary shapes, and can accurately recognize the evolving behaviors of real-time data streams.
nonlinear adaptive distance metric learning for clustering. a good distance metric is crucial for many data mining tasks. to learn a metric in the unsupervised setting, most metric learning algorithms project observed data to a low-dimensional manifold, where geometric relationships such as pairwise distances are preserved. it can be extended to the nonlinear case by applying the kernel trick, which embeds the data into a feature space by specifying the kernel function that computes the dot products between data points in the feature space. in this paper, we propose a novel unsupervised nonlinear adaptive metric learning algorithm, called naml, which performs clustering and distance metric learning simultaneously. naml firstmaps the data to a high-dimensional space through a kernel function; then applies a linear projection to find a low-dimensional manifold where the separability of the data is maximized; and finally performs clustering in the low-dimensional space. the performance of naml depends on the selection of the kernel function and the projection. we show that the joint kernel learning, dimensionality reduction, and clustering can be formulated as a trace maximization problem, which can be solved via an iterative procedure in the em framework. experimental results demonstrated the efficacy of the proposed algorithm.
cross-language information retrieval using parafac2. a standard approach to cross-language information retrieval (clir) uses latent semantic analysis (lsa) in conjunction with a multilingual parallel aligned corpus. this approach has been shown to be successful in identifying similar documents across languages - or more precisely, retrieving the most similar document in one language to a query in another language. however, the approach has severe drawbacks when applied to a related task, that of clustering documents "language-independently", so that documents about similar topics end up closest to one another in the semantic space regardless of their language. the problem is that documents are generally more similar to other documents in the same language than they are to documents in a different language, but on the same topic. as a result, when using multilingual lsa, documents will in practice cluster by language, not by topic. we propose a novel application of parafac2 (which is a variant of parafac, a multi-way generalization of the singular value decomposition [svd]) to overcome this problem. instead of forming a single multilingual term-by-document matrix which, under lsa, is subjected to svd, we form an irregular three-way array, each slice of which is a separate term-by-document matrix for a single language in the parallel corpus. the goal is to compute an svd for each language such that v (the matrix of right singular vectors) is the same across all languages. effectively, parafac2 imposes the constraint, not present in standard lsa, that the "concepts" in all documents in the parallel corpus are the same regardless of language. intuitively, this constraint makes sense, since the whole purpose of using a parallel corpus is that exactly the same concepts are expressed in the translations. we tested this approach by comparing the performance of parafac2 with standard lsa in solving a particular clir problem. from our results, we conclude that parafac2 offers a very promising alternative to lsa not only for multilingual document clustering, but also for solving other problems in cross-language information retrieval.
evolutionary spectral clustering by incorporating temporal smoothness. evolutionary clustering is an emerging research area essential to important applications such as clustering dynamic web and blog contents and clustering data streams. in evolutionary clustering, a good clustering result should fit the current data well, while simultaneously not deviate too dramatically from the recent history. to fulfill this dual purpose, a measure of temporal smoothness is integrated in the overall measure of clustering quality. in this paper, we propose two frameworks that incorporate temporal smoothness in evolutionary spectral clustering. for both frameworks, we start with intuitions gained from the well-known k-means clustering problem, and then propose and solve corresponding cost functions for the evolutionary spectral clustering problems. our solutions to the evolutionary spectral clustering problems provide more stable and consistent clustering results that are less sensitive to short-term noises while at the same time are adaptive to long-term cluster drifts. furthermore, we demonstrate that our methods provide the optimal solutions to the relaxed versions of the corresponding evolutionary k-means clustering problems. performance experiments over a number of real and synthetic data sets illustrate our evolutionary spectral clustering methods provide more robust clustering results that are not sensitive to noise and can adapt to data drifts.
structural and temporal analysis of the blogosphere through community factorization. the blogosphere has unique structural and temporal properties since blogs are typically used as communication media among human individuals. in this paper, we propose a novel technique that captures the structure and temporal dynamics of blog communities. in our framework, a community is a set of blogs that communicate with each other triggered by some events (such as a news article). the community is represented by its structure and temporal dynamics: a community graph indicates how often one blog communicates with another, and a community intensity indicates the activity level of the community that varies over time. our method, community factorization, extracts such communities from the blogosphere, where the communication among blogs is observed as a set of subgraphs (i.e., threads of discussion). this community extraction is formulated as a factorization problem in the framework of constrained optimization, in which the objective is to best explain the observed interactions in the blogosphere over time. we further provide a scalable algorithm for computing solutions to the constrained optimization problems. extensive experimental studies on both synthetic and real blog data demonstrate that our technique is able to discover meaningful communities that are not detectable by traditional methods.
discovering the hidden structure of house prices with a non-parametric latent manifold model. in many regression problems, the variable to be predicted depends not only on a sample-specific feature vector, but also on an unknown (latent) manifold that must satisfy known constraints. an example is house prices, which depend on the characteristics of the house, and on the desirability of the neighborhood, which is not directly measurable. the proposed method comprises two trainable components. the first one is a parametric model that predicts the "intrinsic" price of the house from its description. the second one is a smooth, non-parametric model of the latent "desirability" manifold. the predicted price of a house is the product of its intrinsic price and desirability. the two components are trained simultaneously using a deterministic form of the em algorithm. the model was trained on a large dataset of houses from los angeles county. it produces better predictions than pure parametric and non-parametric models. it also produces useful estimates of the desirability surface at each location.
stochastic processes and temporal data mining. this article tries to give an answer to a fundamental question intemporal data mining: "under what conditions a temporal rule extracted from up-to-date temporal data keeps its confidence/support for future data". a possible solution is given by using, on the one hand, a temporal logic formalism which allows the definition of the main notions (event, temporal rule, support, confidence) in a formal way and, on the other hand, the stochastic limit theory. under this probabilistic temporal framework, the equivalence between the existence of the support of a temporal rule and the law of large numbers is systematically analyzed.
exploiting underrepresented query aspects for automatic query expansion. users attempt to express their search goals through web search queries. when a search goal has multiple components or aspects, documents that represent all the aspects are likely to be more relevant than those that only represent some aspects. current web search engines often produce result sets whose top ranking documents represent only a subset of the query aspects. by expanding the query using the right keywords, the search engine can find documents that represent more query aspects and performance improves. this paper describes abraq, an approach for automatically finding the right keywords to expand the query. abraq identifies the aspects in the query, identifies which aspects are underrepresented in the result set of the original query, and finally, for any particularly underrepresented aspect, identifies keywords that would enhance that aspect's representation and automatically expands the query using the best one. the paper presents experiments that show abraq significantly increases the precision of hard queries, whereas traditional automatic query expansion techniques have not improved precision. abraq also compared favourably against a range of interactive query expansion techniques that require user involvement including clustering, web-log analysis, relevance feedback, and pseudo relevance feedback.
canonicalization of database records using adaptive similarity measures. it is becoming increasingly common to construct databases from information automatically culled from many heterogeneous sources. for example, a research publication database can be constructed by automatically extracting titles, authors, and conference information from online papers. a common difficulty in consolidating data from multiple sources is that records are referenced in a variety of ways (e.g. abbreviations, aliases, and misspellings). therefore, it can be difficult to construct a single, standard representation to present to the user. we refer to the task of constructing this representation as canonicalization. despite its importance, there is little existing work on canonicalization. in this paper, we explore the use of edit distance measures to construct a canonical representation that is "central" in the sense that it is most similar to each of the disparate records. this approach reduces the impact of noisy records on the canonical representation. furthermore, because the user may prefer different styles of canonicalization, we show how different edit distance costs can result in different forms of canonicalization. for example, reducing the cost of character deletions can result in representations that favor abbreviated forms over expanded forms (e.g. kdd versus conference on knowledge discovery and data mining). we describe how to learn these costs from a small amount of manually annotated data using stochastic hill-climbing. additionally, we investigate feature-based methods to learn ranking preferences over canonicalizations. these approaches can incorporate arbitrary textual evidence to select a canonical record. we evaluate our approach on a real-world publications database and show that our learning method results in a canonicalization solution that is robust to errors and easily customizable to user preferences.
detecting changes in large data sets of payment card data: a case study. an important problem in data mining is detecting changes in large datasets. although there are a variety of change detection algorithms that have been developed, in practice it can be a problem to scale these algorithms to large data sets due to the heterogeneity of the data. in this paper, we describe a case study involving payment card data in which we built and monitored a separate change detection model for each cell in a multi-dimensional data cube. we describe a system that has been in operation for the past two years that builds and monitors over 15,000 separate baseline models and the process that isused for generating and investigating alerts using these baselines.
co-clustering based classification for out-of-domain documents. in many real world applications, labeled data are in short supply. it often happens that obtaining labeled data in a new domain is expensive and time consuming, while there may be plenty of labeled data from a related but different domain. traditional machine learning is not able to cope well with learning across different domains. in this paper, we address this problem for a text-mining task, where the labeled data are under one distribution in one domain known as in-domain data, while the unlabeled data are under a related but different domain known as out-of-domain data. our general goal is to learn from the in-domain and apply the learned knowledge to out-of-domain. we propose a co-clustering based classification (cocc) algorithm to tackle this problem. co-clustering is used as a bridge to propagate the class structure and knowledge from the in-domain to the out-of-domain. we present theoretical and empirical analysis to show that our algorithm is able to produce high quality classification results, even when the distributions between the two data are different. the experimental results show that our algorithm greatly improves the classification performance over the traditional learning algorithms.
text classification, business intelligence, and interactivity: automating c-sat analysis for services industry. text classification has matured as a research discipline over the last decade. independently, business intelligence over structured databases has long been a source of insights for enterprises. in this work, we bring the two together for customer satisfaction(c-sat) analysis in the services industry. we present itacs, a solution combining text classification and business intelligence integrated with a novel interactive text labeling interface. itacs has been deployed in multiple client accounts in contact centers. it can be extended to any services industry setting to analyze unstructured text data and derive operational and business insights. we highlight importance of interactivity in real-life text classification settings. we bring out some unique research challenges about label-sets, measuring accuracy, and interpretability that need serious attention in both academic and industrial research. we recount invaluable experiences and lessons learned as data mining researchers working toward seeing research technology deployed in the services industry.
feature selection methods for text classification. we consider feature selection for text classification both theoretically and empirically. our main result is an unsupervised feature selection strategy for which we give worst-case theoretical guarantees on the generalization power of the resultant classification function f with respect to the classification function f obtained when keeping all the features. to the best of our knowledge, this is the first feature selection method with such guarantees. in addition, the analysis leads to insights as to when and why this feature selection strategy will perform well in practice. we then use the techtc-100, 20-newsgroups, and reuters-rcv2 data sets to evaluate empirically the performance of this and two simpler but related feature selection strategies against two commonly-used strategies. our empirical evaluation shows that the strategy with provable performance guarantees performs well in comparison with other commonly-used feature selection strategies. in addition, it performs better on certain datasets under very aggressive feature selection.
efficient incremental constrained clustering. clustering with constraints is an emerging area of data mining research. however, most work assumes that the constraints are given as one large batch. in this paper we explore the situation where the constraints are incrementally given. in this way the user after seeing a clustering can provide positive and negative feedback via constraints to critique a clustering solution. we consider the problem of efficiently updating a clustering to satisfy the new and old constraints rather than reclustering the entire data set. we show that the problem of incremental clustering under constraints is np-hard in general, but identify several sufficient conditions which lead to efficiently solvable versions. these translate into a set of rules on the types of constraints thatcan be added and constraint set properties that must be maintained. we demonstrate that this approach is more efficient than re-clustering the entire data set and has several other advantages.
a framework for simultaneous co-clustering and learning from complex data. for difficult classification or regression problems, practitioners often segment the data into relatively homogenous groups and then build a model for each group. this two-step procedure usually results in simpler, more interpretable and actionable models without any lossin accuracy. we consider problems such as predicting customer behavior across products, where the independent variables can be naturally partitioned into two groups. a pivoting operation can now result in the dependent variable showing up as entries in a "customer by product" data matrix. we present a model-based co-clustering (meta)-algorithm that interleaves clustering and construction of prediction models to iteratively improve both cluster assignment and fit of the models. this algorithm provably converges to a local minimum of a suitable cost function. the framework not only generalizes co-clustering and collaborative filtering to model-basedco-clustering, but can also be viewed as simultaneous co-segmentation and classification or regression, which is better than independently clustering the data first and then building models. moreover, it applies to a wide range of bi-modal or multimodal data, and can be easily specialized to address classification and regression problems. we demonstrate the effectiveness of our approach on both these problems through experimentation on real and synthetic data.
using predictive analysis to improve invoice-to-cash collection. it is commonly agreed that accounts receivable (ar) can be a source of financial difficulty for firms when they are not efficiently managed and are underperforming. experience across multiple industries shows that effective management of ar and overall financial performance of firms are positively correlated. in this paper we address the problem of reducing outstanding receivables through improvements in the collections strategy. specifically, we demonstrate how supervised learning can be used to build models for predicting the payment outcomes of newly-created invoices, thus enabling customized collection actions tailored for each invoice or customer. our models can predict with high accuracy if an invoice will be paid on time or not and can provide estimates of the magnitude of the delay. we illustrate our techniques in the context of real-world transaction data from multiple firms. finally, simulation results show that our approach can reduce collection time up to a factor of four compared to a baseline that is not model-driven.
development of neuroelectromagnetic ontologies(nemo): a framework for mining brainwave ontologies. event-related potentials (erp) are brain electrophysiological patterns created by averaging electroencephalographic (eeg) data, time-locking to events of interest (e.g., stimulus or response onset). in this paper, we propose a generic framework for mining anddeveloping domain ontologies and apply it to mine brainwave (erp) ontologies. the concepts and relationships in erp ontologies can be mined according to the following steps: pattern decomposition, extraction of summary metrics for concept candidates, hierarchical clustering of patterns for classes and class taxonomies, and clustering-based classification and association rules mining for relationships (axioms) of concepts. we have applied this process to several dense-array (128-channel) erp datasets. results suggest good correspondence between mined concepts and rules, on the one hand, and patterns and rules that were independently formulated by domain experts, on the other. data mining results also suggest ways in which expert-defined rules might be refined to improve ontologyrepresentation and classification results. the next goal of our erp ontology mining framework is to address some long-standing challenges in conducting large-scale comparison and integration of results across erp paradigms and laboratories. in a more general context, this work illustrates the promise of an interdisciplinary research program, which combines data mining, neuroinformatics andontology engineering to address real-world problems.