Detecting localized homogeneous anomalies over spatio-temporal data

Telang, Aditya; Deepak, P; Joshi, Salil; Deshpande, Prasad M.; Rajendran, Ranjana

doi:10.1007/s10618-014-0366-x

Cited by 26 publications

(17 citation statements)

References 33 publications

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“…This measure is a straightforward adaptation of the Gini index, 17 a measure of statistical dispersion that has been used within data mining settings earlier (e.g., [32]). Similar to the construction of the Q(., .)…”

Section: Gini Index Analysismentioning

confidence: 99%

Leveraging semantic resources in diversified query expansion

et al. 2017

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A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. In this paper, we consider the usage of semantic resources and tools to arrive at improved methods for diversified query expansion. In particular, we develop two methods, those that leverage Wikipedia and pre-learnt distributional word embeddings respectively. Both the approaches operate on a common three-phase framework; that of first taking a set of informative terms from the search results of the initial query, then building a graph, following by using a diversity-conscious node ranking to prioritize candidate terms for diversified query expansion. Our methods differ in the second phase, with the first method Select-Link-Rank (SLR) linking terms with Wikipedia entities to accomplish graph construction; on the other hand, our second method, SelectEmbed-Rank (SER), constructs the graph using similarities between distributional word embeddings. Through an empirical analysis and user study, we show that SLR ourperforms state-of-the-art diversified query expansion methods, thus establishing that Wikipedia is an effective resource to aid diversified query expansion. Our empirical analysis also illustrates that SER outperforms the baselines convincingly, asserting that it is the best available method for those cases where SLR is not applicable; these include narrow-focus search systems where a relevant knowledge base is unavailable. Our SLR method is also seen to outperform a state-of-the-art method in the task of diversified entity ranking.

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Section: Gini Index Analysismentioning

confidence: 99%

Leveraging semantic resources in diversified query expansion

et al. 2017

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“…In our example, the hot desert wont qualify as an anomaly as long as the contrast in warmth is not good enough with the surrounding plain, regardless of whether it is much warmer than the average temperature in the whole area under consideration. Under this category, we will describe the LHA anomaly detection method [33] and also comment on how image segmentation techniques are applicable for local anomaly detection. -Grouping: Unlike anomaly detection methods that specify a context for comparison crisply, methods under this category use an indirect form of context comparison for anomaly estimation.…”

Section: Taxonomy Of Techniquesmentioning

confidence: 99%

“…LHA detection [33] addresses the problem of discovering regions that are spatially coherent and homogeneous on the value attributes, while also contrasting well on the value attributes with their generalized local neighborhood. This is designed to work on gridded or tessellated data, where the adjacency/neighborhood relation is well-defined.…”

Section: Localized Homogeneous Anomaliesmentioning

confidence: 99%

“…4a is not considered as an LHA due to being of medium intensity, which would not differ much from the average intensity of its neighborhood (which is composed of two halves of low and high intensity). Telang et al [33] propose that it be considered a non-anomalous transitional region of intermediate values between the low values in the left and the high values in the right. On the other hand, Fig.…”

Section: Localized Homogeneous Anomaliesmentioning

confidence: 99%

“…This is a simple adaptation of hierarchical agglomerative clustering [36], proposed in [33] as a technique to identify spatially contiguous clusters. Hierarchical agglomerative clustering works by initially assigning all objects to singleton clusters, followed by merging the most similar pair of clusters iteratively, until only the desired number of clusters remains.…”

Section: Hac-amentioning

confidence: 99%

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Unsupervised Learning Algorithms

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The problem of detecting spatially-coherent groups of data that exhibit anomalous behavior has started to attract attention due to applications across areas such as epidemic analysis and weather forecasting. Earlier efforts from the data mining community have largely focused on finding outliers, individual data objects that display deviant behavior. Such point-based methods are not easy to extend to find groups of data that exhibit anomalous behavior. Scan statistics are methods from the statistics community that have considered the problem of identifying regions where data objects exhibit a behavior that is atypical of the general dataset. The spatial scan statistic and methods that build upon it mostly adopt the framework of defining a character for regions (e.g., circular or elliptical) of objects and repeatedly sampling regions of such character followed by applying a statistical test for anomaly detection. In the past decade, there have been efforts from the statistics community to enhance efficiency of scan statistics as well as to enable discovery of arbitrarily shaped anomalous regions. On the other hand, the data mining community has started to look at determining anomalous regions that have behavior divergent from their neighborhood. In this chapter, we survey the space of techniques for detecting anomalous regions on spatial data from across the data mining and statistics communities while outlining connections to wellstudied problems in clustering and image segmentation. We analyze the techniques systematically by categorizing them appropriately to provide a structured birds-eye view of the work on anomalous region detection; we hope that this would encourage better cross-pollination of ideas across communities to help advance the frontier in anomaly detection.

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Anomalous cluster detection in spatiotemporal meteorological fields

Ramachandra

Dutton

Vatsavai

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Statistical Analysis

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Finding anomalous regions in spatiotemporal climate data is an important problem with a need for greater accuracy. The collective and contextual nature of anomalies (e.g., heat waves) coupled with the real‐valued, seasonal, multimodal, highly correlated, and gridded nature of climate variable observations poses a multitude of challenges. Existing anomaly detection methods have limitations in the specific setting of real‐valued areal spatiotemporal data. In this paper, we develop a method for extreme event detection in meteorological datasets that follows from well known distribution‐based anomaly detection approaches. The method models spatial and temporal correlations explicitly through a piecewise parametric assumption and generalizes the Mahalanobis distance across distributions of different dimensionalities. The result is an effective method to mine contiguous spatiotemporal anomalous regions from meteorological fields which improves upon the current standard approach in climatology. The proposed method has been evaluated on a real global surface temperature dataset and validated using historical records of extreme events.

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Detecting localized homogeneous anomalies over spatio-temporal data

Cited by 26 publications

References 33 publications

Leveraging semantic resources in diversified query expansion

Leveraging semantic resources in diversified query expansion

Anomaly Detection for Data with Spatial Attributes

Anomalous cluster detection in spatiotemporal meteorological fields

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