Least-squares community extraction in feature-rich networks using similarity data

We explore a doubly-greedy approach to the issue of community detection in feature-rich networks. According to this approach, both the network and feature data are straightforwardly recovered from the underlying unknown non-overlapping communities, supplied with a center in the feature space and intensity weight(s) over the network each. Our least-squares additive criterion allows us to search for communities one-by-one and to find each community by adding entities one by one. A focus of this paper is that the feature-space data part is converted into a similarity matrix format. The similarity/link values can be used in either of two modes: (a) as measured in the same scale so that one may can meaningfully compare and sum similarity values across the entire similarity matrix (summability mode), and (b) similarity values in one column should not be compared with the values in other columns (nonsummability mode). The two input matrices and two modes lead us to developing four different Iterative Community Extraction from Similarity data (ICESi) algorithms, which determine the number of communities automatically. Our experiments at real-world and synthetic datasets show that these algorithms are valid and competitive.

Implementasi Metode Apriori Dalam Pembagian Kejuruan Penerimaan Siswa Baru Di SMK Negeri 1 Siantar

The purpose of this research is to determine the vocational division of the acceptance of new students that will be taken by the author with data mining techniques using a priori algorithm method. The data source used is to make observations. Match predictions can be obtained based on the results of comparisons with other students who have similarity data with student A. By using a priori algorithm obtained results that involve a collection of items that often with a high value of trust. The results of this study are data that group prospective new students based on their desired majors with a minimum support of 50% and a minimum trust of 50%, making 20 rules that are set aside. One of the rules that is formed is if the student chooses a fashion major (A4) then the department that is more suitable for students is hair and skin (A2) with a support value of 0.5 or equal to 50 and trust 100 to 0.5 or equal to 50 It is hoped that this information can provide advice to the public vocational school 1 Siantar.

The Speech Act of Cartoon Movie: Spongebob Squarepants’ The Movie

This research aims to reveal: the types of speech acts used by the main character in “Spongebob Squarepants’ the movie and the previous studies in speech act analysis for knowing the way of the directives of speech act appears. It employed descriptive qualitative research in the explaining of speech acts types used by the main character. There is still a lack of research about the analysis of speech act categories in cartoon movie or animation movie, it is substantial to be carried out. The finding of this research is the directives speech act is the most frequently in SpongeBob SquarePants the movie with data 118 or 44,36% from a total of 266 or 100% of the whole data. Then, the similarity data found in the three journals about analysis of speech act with data that the directives speech act is the highest utterance in a cartoon movie.

Whether the Pairwise Rating Method and the Spatial Arrangement Method Yield Comparable Dimensionalities Depends on the Dimensionality Choice Procedure

We investigate whether two methods for obtaining similarity data yield multidimensional scaling (MDS) solutions of comparable dimensionality. In the Pairwise Rating Method (PRaM), participants rate the (dis)similarity of all pairs of stimuli on a Likert scale. In the Spatial Arrangement Method (SpAM), participants organize stimuli on a computer screen so that the distance between stimuli represents their perceived dissimilarity. Across two studies that included eight semantic categories with varying numbers of both pictorial and verbal exemplars, we did not find consistent dimensionality differences between the two similarity measurement methods. The results alleviate the concern that because of its two-dimensional nature, SpAM might underestimate the dimensionality of high-dimensional stimuli compared to PRaM. Aggregating the SpAM similarity data from a sufficient number of participants can yield spatial representations with more than two dimensions. However, the resulting number of dimensions was found to be highly dependent on the dimensionality choice procedure. Even for specific combinations of a single category and similarity measurement method, different dimensionalities were obtained depending on whether the reliability of the similarity data, Monte Carlo simulations, or predictive correlations were used to establish the number of dimensions, indicating the need for a more systematic investigation into dimensionality selection for MDS.