Polymorphs of Octaphenylcyclotetrasiloxane

Baruah, Abhilasha M.; Karmakar, Anirban; Baruah, Jubaraj B.

doi:10.1021/bk-2010-1051.ch003

Cited by 4 publications

(4 citation statements)

References 9 publications

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“…y (1) =min(y 1 , y 2 ), and y (2) =max(y 1 , y 2 ) (2) Explains why if two line segments are superimposed, the common portion looks doubly dark [5]. The identity (2) (2) on the two equi-fuzzy intervals it can be written as…”

Section: Set Superimpositionmentioning

confidence: 99%

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The Weekly Mining of Fuzzy Patterns from Temporal Datasets

Husamuddin¹

2017

IJAIS

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The process of extracting fuzzy patterns from temporal datasets is a well known data mining problem. Weekly pattern is one such example where it reflects a pattern with some fuzzy time interval every week. This process involves two steps. Firstly, it finds frequent sets and secondly, it finds the association rules that occur in certain time intervals weekly. Most of the fuzzy patterns are concentrated as user defined. However, the probability of user not having prior knowledge of datasets being used in some applications is more. Thus, resulting in the loss of fuzziness related to the problem. The limitation of the natural language also bounds the user in specifying the same. This paper, proposes a method of extracting patterns that occur weekly in a particular fuzzy time frame and the fuzzy time frame is generated by the method itself. The efficacy of the method is backed by the experimental results

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Section: Set Superimpositionmentioning

confidence: 99%

“…In [4], it turns out to be a fuzzy time interval. In [5], a superimposition method is used for overlapping the frequency of intervals. Considering the time-stamp as year_month_day_hour_minute_second, methods were proposed in [6,7,8,9], for extracting yearly, monthly, and daily fuzzy frequent itemsets.…”

Section: Introductionmentioning

confidence: 99%

The Weekly Mining of Fuzzy Patterns from Temporal Datasets

Husamuddin¹

2017

IJAIS

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“…1Where (AB) (2) are the elements of (AB) represented twice, and (+) represents union of disjoint sets. Where a(1)=min(a1, a2) a(2)=max(a1, a2) b(1)=min(b1, b2), and b(2)=max(b1, b2) (2) Explains why if two line segments are superimposed, the common portion peeps doubly dark [5]. The identity 2is called thefundamental identity of superimposition of intervals.…”

Section: Set Superimpositionmentioning

confidence: 99%

“…For X and Y if the experiential probability distribution functions 1(x) and 2(y) are defined as in (5) and (6) respectively. Then, the Glivenko-Cantelli Lemma of order statistics states that the mathematical expectation of the empirical probability distributions would be given by the respective theoretical probability distributions.…”

Section: Lemma 1 (The Glivenko-cantelli Lemmaof Order Statistics)mentioning

confidence: 99%

The Mining Hourly Fuzzy Patterns from Temporal Datasets

Mazarbhuiya¹,

Perwej²

2015

IJERT

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The retrace patterns from supermarket datasets is an interesting data mining issue. The hourly fuzzy pattern is an example of such patterns where the pattern holds in some fuzzy time interval in each hour. The issue involves first frequent set mining and then deduces association rules from the frequent sets. We call it hourly fuzzy patterns. In the beginning works were mainly concentrated about a userspecified fuzzy pattern mining. In as much as, in some applications,the user may not have previous knowledge about the datasets under opinion, so may not be able to specify fuzzy time interval. Identical may happen due to the boundary of natural language. In this paper, we are intending a method of finding such patterns. The nicety about the method is that the fuzzy time intervals are generated by the method itself. The impact of our method is exhibited with experimental results.

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