Automatic BIRCH thresholding with features transformation for hierarchical breast cancer clustering

<p>Breast cancer is one of the most common diseases diagnosed in women over the world. The balanced iterative reducing and clustering using hierarchies (BIRCH) has been widely used in many applications. However, clustering the patient records and selecting an optimal threshold for the hierarchical clusters still a challenging task. In addition, the existing BIRCH is sensitive to the order of data records and influenced by many numerical and functional parameters. Therefore, this paper proposes a unique BIRCH-based algorithm for breast cancer clustering. We aim at transforming the medical records using the breast screening features into sub-clusters to group the subject cases into malignant or benign clusters. The basic BIRCH clustering is firstly fed by a set of normalized features then we automate the threshold initialization to enhance the tree-based sub-clustering procedure. Additionally, we present a thorough analysis on the performance impact of tuning BIRCH with various relevant linkage functions and similarity measures. Two datasets of the standard breast cancer wisconsin (BCW) benchmarking collection are used to evaluate our algorithm. The experimental results show a clustering accuracy of 97.7% in 0.0004 seconds only, thereby confirming the efficiency of the proposed method in clustering the patient records and making timely decisions.</p>

EMD Based Semantic User Similarity using Past Travel Histories

The cost-effective and easy availability of handheld mobile devices and ubiquity of location acquisition services such as GPS and GSM networks has helped expedient logging and sharing of location histories of mobile users. This work aims to find semantic user similarity using their past travel histories. Application of the semantic similarity measure can be found in tourism-related recommender systems and information retrieval. The paper presents Earth Mover’s Distance (EMD) based semantic user similarity measure using users' GPS logs. The similarity measure is applied and evaluated on the GPS dataset of 182 users collected from April 2007 to August 2012 by Microsoft's GeoLife project. The proposed similarity measure is compared with conventional similarity measures used in literature such as Jaccard, Dice, and Pearsons’ Correlation. The percentage improvement of EMD based approach over existing approaches in terms of average RMSE is 10.70%, and average MAE is 5.73%.

EMD-Based Semantic User Similarity Using Past Travel Histories

The cost-effective and easy availability of handheld mobile devices and ubiquity of location acquisition services such as GPS and GSM networks has helped expedient logging and sharing of location histories of mobile users. This work aims to find semantic user similarity using their past travel histories. Application of the semantic similarity measure can be found in tourism-related recommender systems and information retrieval. The paper presents Earth Mover’s Distance (EMD) based semantic user similarity measure using users' GPS logs. The similarity measure is applied and evaluated on the GPS dataset of 182 users collected from April 2007 to August 2012 by Microsoft's GeoLife project. The proposed similarity measure is compared with conventional similarity measures used in literature such as Jaccard, Dice, and Pearsons’ Correlation. The percentage improvement of EMD based approach over existing approaches in terms of average RMSE is 10.70%, and average MAE is 5.73%.

Expectation violations, expectation change, and expectation persistence: The scientific landscape as revealed by bibliometric network analyses

Expectation violations occur when there is a discrepancy between expected and perceived events or experiences. However, expectations often persist despite disconfirming evidence. Therefore, research on expectation violations, expectation change, and expectation persistence has been conducted in several fields of psychology with wide-ranging theoretical assumptions and empirical considerations. In the present review, we analyzed how these research fields relate to each other via bibliometric network analyses. For this purpose, we conducted a systematic literature search to identify scientific publications on expectation violations, expectation change, and expectation persistence. The literature corpus was then quantitatively analyzed using similarity measures that allow a data-driven classification of publications into groups, revealing their conceptual, theoretical, and empirical commonalities. Our results indicate that many influential publications have focused on finding reactivity measures (e.g., brain activation) to the discrepancy experienced between expectations and outcomes. Furthermore, these measures have been used to assess when and to which degree learning and behavioral adaptation (i.e., expectation change) takes place. We discuss the potential application of these measures for understanding expectation violations in more complex settings (e.g., social interaction) as well as phenomena such as expectation persistence. The goal of this review was to foster interdisciplinarity in psychology, enabling scientists and practitioners to identify new topics, promising empirical approaches and previously neglected variables.

Similarity measures for Fermatean fuzzy sets and its applications in group decision-making

The intention of this paper is to propose some similarity measures between Fermatean fuzzy sets (FFSs). Firstly, we propose some score based similarity measures for finding similarity measures of FFSs and also propose score based cosine similarity measures between FFSs. Furthermore, we introduce three newly scored functions for effective uses of Fermatean fuzzy sets and discuss some relevant properties of cosine similarity measure. Fermatean fuzzy sets introduced by Senapati and Yager can manipulate uncertain information more easily in the process of multi-criteria decision making (MCDM) and group decision making. Here, we investigate score based similarity measures of Fermatean fuzzy sets and scout the uses of FFSs in pattern recognition. Based on different types of similarity measures a pattern recognition problem viz. personnel appointment is presented to describe the use of FFSs and its similarity measure as well as scores. The counterfeit results show that the proposed method is more malleable than the existing method(s). Finally, concluding remarks and the scope of future research of the proposed approach are given.