Search (51 results, page 3 of 3)

2.5475924E-4 = product of:
  0.004330907 = sum of:
    0.004330907 = weight(_text_:in in 2708) [ClassicSimilarity], result of:
      0.004330907 = score(doc=2708,freq=4.0), product of:
        0.033961542 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.024967048 = queryNorm
        0.12752387 = fieldWeight in 2708, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=2708)
  0.05882353 = coord(1/17)

Abstract

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.

2.5475924E-4 = product of:
  0.004330907 = sum of:
    0.004330907 = weight(_text_:in in 4994) [ClassicSimilarity], result of:
      0.004330907 = score(doc=4994,freq=4.0), product of:
        0.033961542 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.024967048 = queryNorm
        0.12752387 = fieldWeight in 4994, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=4994)
  0.05882353 = coord(1/17)

Abstract

Contrary to what one might expect, Nobel laureates and Fields medalists have a rather large fraction (10% or more) of uncited publications. This is the case for (in total) 75 examined researchers from the fields of mathematics (Fields medalists), physics, chemistry, and physiology or medicine (Nobel laureates). We study several indicators for these researchers, including the h-index, total number of publications, average number of citations per publication, the number (and fraction) of uncited publications, and their interrelations. The most remarkable result is a positive correlation between the h-index and the number of uncited articles. We also present a Lotkaian model, which partially explains the empirically found regularities.

Footnote

Vgl.: Erratum. In: Journal of the American Society for Information Science and Technology. 63(2012) no.2, S.429.

2.401893E-4 = product of:
  0.004083218 = sum of:
    0.004083218 = weight(_text_:in in 4394) [ClassicSimilarity], result of:
      0.004083218 = score(doc=4394,freq=2.0), product of:
        0.033961542 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.024967048 = queryNorm
        0.120230645 = fieldWeight in 4394, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0625 = fieldNorm(doc=4394)
  0.05882353 = coord(1/17)

Abstract

Discusses the number of actions (or time) needed to organize library shelves. Studies 2 types pf problem: organizing a library shelf out of an unordered pile of books, and putting an existing shelf of books in the rough order. Uses results from information theory as well as from rank order statistics (runs). Draws conclusions about the advised frequency with which these actions should be undertaken

2.401893E-4 = product of:
  0.004083218 = sum of:
    0.004083218 = weight(_text_:in in 4385) [ClassicSimilarity], result of:
      0.004083218 = score(doc=4385,freq=2.0), product of:
        0.033961542 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.024967048 = queryNorm
        0.120230645 = fieldWeight in 4385, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0625 = fieldNorm(doc=4385)
  0.05882353 = coord(1/17)

Abstract

Observed aging curves are influenced by publication delays. In this article, we show how the 'undisturbed' aging function and the publication delay combine to give the observed aging function. This combination is performed by a mathematical operation known as convolution. Examples are given, such as the convolution of 2 Poisson distributions, 2 exponential distributions, a 2 lognormal distributions. A paradox is observed between theory and real data

2.401893E-4 = product of:
  0.004083218 = sum of:
    0.004083218 = weight(_text_:in in 3598) [ClassicSimilarity], result of:
      0.004083218 = score(doc=3598,freq=2.0), product of:
        0.033961542 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.024967048 = queryNorm
        0.120230645 = fieldWeight in 3598, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0625 = fieldNorm(doc=3598)
  0.05882353 = coord(1/17)

Abstract

A graph in van Eck and Waltman [JASIST, 60(8), 2009, p. 1644], representing the relation between the association strength and the cosine, is partially explained as a sheaf of parabolas, each parabola being the functional relation between these similarity measures on the trajectories x*y=a, a constant. Based on earlier obtained relations between cosine and other similarity measures (e.g., Jaccard index), we can prove new relations between the association strength and these other measures.

2.1016564E-4 = product of:
  0.0035728158 = sum of:
    0.0035728158 = weight(_text_:in in 2531) [ClassicSimilarity], result of:
      0.0035728158 = score(doc=2531,freq=2.0), product of:
        0.033961542 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.024967048 = queryNorm
        0.10520181 = fieldWeight in 2531, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0546875 = fieldNorm(doc=2531)
  0.05882353 = coord(1/17)

Abstract

It is shown that vector information retrieval (IR) and general fuzzy IR uses two types of fuzzy set operations: the original "Zadeh min-max operations" and the so-called "probabilistic sum and algebraic product operations". The universal IR surface, valid for classical 0-1 IR (i.e. where ordinary sets are used) and used in IR evaluation, is extended to and reproved for vector IR, using the probabilistic sum and algebraic product model. We also show (by counterexample) that, using the "Zadeh min-max" fuzzy model, yields a breakdown of this IR surface.

2.1016564E-4 = product of:
  0.0035728158 = sum of:
    0.0035728158 = weight(_text_:in in 243) [ClassicSimilarity], result of:
      0.0035728158 = score(doc=243,freq=2.0), product of:
        0.033961542 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.024967048 = queryNorm
        0.10520181 = fieldWeight in 243, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0546875 = fieldNorm(doc=243)
  0.05882353 = coord(1/17)

Abstract

Based on earlier results about the shifted Lotka function, we prove an implicit functional relation between the Hirsch index (h-index) and the total number of sources (T). It is shown that the corresponding function, h(T), is concavely increasing. Next, we construct an implicit relation between the h-index and the impact factor IF (an average number of items per source). The corresponding function h(IF) is increasing and we show that if the parameter C in the numerator of the shifted Lotka function is high, then the relation between the h-index and the impact factor is almost linear.

2.1016564E-4 = product of:
  0.0035728158 = sum of:
    0.0035728158 = weight(_text_:in in 463) [ClassicSimilarity], result of:
      0.0035728158 = score(doc=463,freq=2.0), product of:
        0.033961542 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.024967048 = queryNorm
        0.10520181 = fieldWeight in 463, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0546875 = fieldNorm(doc=463)
  0.05882353 = coord(1/17)

Abstract

The author presents a different view on properties of impact measures than given in the paper of De Visscher (2011). He argues that a good impact measure works better when citations are concentrated rather than spread out over articles. The author also presents theoretical evidence that the g-index and the R-index can be close to the square root of the total number of citations, whereas this is not the case for the A-index. Here the author confirms an assertion of De Visscher.

1.8014197E-4 = product of:
  0.0030624135 = sum of:
    0.0030624135 = weight(_text_:in in 4384) [ClassicSimilarity], result of:
      0.0030624135 = score(doc=4384,freq=2.0), product of:
        0.033961542 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.024967048 = queryNorm
        0.09017298 = fieldWeight in 4384, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=4384)
  0.05882353 = coord(1/17)

Abstract

One aim of science evaluation studies is to determine quantitatively the contribution of different players (authors, departments, countries) to the whole system. This information is then used to study the evolution of the system, for instance to gauge the results of special national or international programs. Taking articles as our basic data, we want to determine the exact relative contribution of each coauthor or each country. These numbers are brought together to obtain country scores, or department scores, etc. It turns out, as we will show in this article, that different scoring methods can yield totally different rankings. Conseqeuntly, a ranking between countries, universities, research groups or authors, based on one particular accrediting methods does not contain an absolute truth about their relative importance

1.8014197E-4 = product of:
  0.0030624135 = sum of:
    0.0030624135 = weight(_text_:in in 3993) [ClassicSimilarity], result of:
      0.0030624135 = score(doc=3993,freq=2.0), product of:
        0.033961542 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.024967048 = queryNorm
        0.09017298 = fieldWeight in 3993, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.046875 = fieldNorm(doc=3993)
  0.05882353 = coord(1/17)

Abstract

Similarity measures, such as the ones of Jaccard, Dice, or Cosine, measure the similarity between two vectors. A good property for similarity measures would be that, if we add a constant vector to both vectors, then the similarity must increase. We show that Dice and Jaccard satisfy this property while Cosine and both overlap measures do not. Adding a constant vector is called, in Lorenz concentration theory, "nominal increase" and we show that the stronger "transfer principle" is not a required good property for similarity measures. Another good property is that, when we have two vectors and if we add one of these vectors to both vectors, then the similarity must increase. Now Dice, Jaccard, Cosine, and one of the overlap measures satisfy this property, while the other overlap measure does not. Also a variant of this latter property is studied.

1.5011833E-4 = product of:
  0.0025520115 = sum of:
    0.0025520115 = weight(_text_:in in 4217) [ClassicSimilarity], result of:
      0.0025520115 = score(doc=4217,freq=2.0), product of:
        0.033961542 = queryWeight, product of:
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.024967048 = queryNorm
        0.07514416 = fieldWeight in 4217, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          1.3602545 = idf(docFreq=30841, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4217)
  0.05882353 = coord(1/17)

Abstract

This paper studies mathematical properties of h-index sequences as developed by Liang [Liang, L. (2006). h-Index sequence and h-index matrix: Constructions and applications. Scientometrics, 69(1), 153-159]. For practical reasons, Liming studies such sequences where the time goes backwards while it is more logical to use the time going forward (real career periods). Both type of h-index sequences are studied here and their interrelations are revealed. We show cases where these sequences are convex, linear and concave. We also show that, when one of the sequences is convex then the other one is concave, showing that the reverse-time sequence, in general, cannot be used to derive similar properties of the (difficult to obtain) forward time sequence. We show that both sequences are the same if and only if the author produces the same number of papers per year. If the author produces an increasing number of papers per year, then Liang's h-sequences are above the "normal" ones. All these results are also valid for g- and R-sequences. The results are confirmed by the h-, g- and R-sequences (forward and reverse time) of the author.