Strong similarity measures for ordered sets of documents in information retrieval

Egghe, Leo; Michel, Christine

doi:10.1016/s0306-4573(01)00051-6

Cited by 32 publications

(31 citation statements)

References 3 publications

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“…8 shows the relation between different measures in capturing the spatio-temporal co-occurrence patterns. An interesting ordering relation on the selectivity of the boolean versions of Overlap, Cosine, Dice, and Jaccard coefficients was shown in [6]. We experimentally confirm it for the real positive numbers that reflect volumes (please see Fig.…”

Section: Impact Of Measuressupporting

confidence: 67%

Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions

Pillai

Angryk

Banda

et al. 2012

2012 IEEE 12th International Conference on Data Mining Workshops

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Spatio-temporal co-occurring patterns represent subsets of event types that occur together in both space and time. In comparison to previous work in this field, we present a general framework to identify spatio-temporal cooccurring patterns for continuously evolving spatio-temporal events that have polygon-like representations. We also propose a set of measures to identify spatio-temporal co-occurring patterns and propose an Apriori-based spatio-temporal cooccurrence mining algorithm to find prevalent spatio-temporal co-occurring patterns for extended spatial representations that evolve over time. We evaluate our framework on real-life data to demonstrate the effectiveness of our measures and the algorithm. We present results highlighting the importance of our measures in identifying spatio-temporal co-occurrence patterns.

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Section: Impact Of Measuressupporting

confidence: 67%

Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions

Pillai

Angryk

Banda

et al. 2012

2012 IEEE 12th International Conference on Data Mining Workshops

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“…In Egghe and Michel (2002), it is explained how to interpret the aforementioned definitions in terms of similarity measures between two vectors…”

Section: Introductionmentioning

confidence: 99%

New relations between similarity measures for vectors based on vector norms

Egghe

2008

J. Am. Soc. Inf. Sci.

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The well-known similarity measures Jaccard, Salton's cosine, Dice, and several related overlap measures for vectors are compared. While general relations are not possible to prove, we study these measures on the "trajectories" of the form − → X = a − → Y , where a > 0 is a constant and · denotes the Euclidean norm of a vector. In this case, direct functional relations between these measures are proved. For Jaccard, we prove that it is a convexly increasing function of Salton's cosine measure, but always smaller than or equal to the latter, hereby explaining a curve, experimentally found by Leydesdorff. All the other measures have a linear relation with Salton's cosine, reducing even to equality, in case a = 1. Hence, for equally normed vectors (e.g., for normalized vectors) we, essentially, only have Jaccard's measure and Salton's cosine measure since all the other measures are equal to the latter.

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“…Again we use k = 4 as input parameter of the clustering algorithm. Three more parameters that have to be set appear in expression (7). The first one is the inertia weight w that starts with value 1.5 and decreases along with the iterations.…”

Section: Pso Approach Resultsmentioning

confidence: 99%

“…The vector representation of documents facilitates the comparison of these by using metrics on the vector space in which they have been represented. Although various metrics are allowed [7], one of those most used in text mining is the cosine similarity or angular separation that determines the similarity amongst documents as the cosine of the angle that forms their respective vectors in the representation space. This metric is easy to calculate and various experimental studies have shown that it obtains better results in text mining than other classic metrics such as the Euclidean distance [8].…”

Section: Feature Selection and Vector Representationmentioning

confidence: 99%

Feature selection strategies for automated classification of digital media content

Cobo

2011

Journal of Information Science

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This paper proposes strategies for feature selection of digital news articles that allow an effective implementation of learning algorithms for the unsupervised classification of news articles. With the appropriate selection of a small subset of features a correct identification of related news can be achieved, thus enabling organizations and individual users to keep track of current events. The paper defines a quality measure of the discriminatory power of each feature and verifies that the selection of a feature subset with higher quality values allows obtaining good classification results. A Particle Swarm Optimization (PSO) based selection method is also proposed. Both proposals are validated on two collections of press clippings collated from news search services in digital media. Experimental results reveal that good classification accuracy can be achieved with small subsets of between 3 per cent and 6 per cent of the features.

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Strong similarity measures for ordered sets of documents in information retrieval

Cited by 32 publications

References 3 publications

Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions

Spatio-temporal Co-occurrence Pattern Mining in Data Sets with Evolving Regions

New relations between similarity measures for vectors based on vector norms

Feature selection strategies for automated classification of digital media content

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