A Cross-Sectional and Temporal Analysis of Information Consumption on Twitter

We report on an exploratory analysis of the similarities and differences among three different forms of information consumption on Twitter viz., following, listing and subscribing. We construct a cross- sectional and temporal framework to analyze the relationships among these three forms. Our analysis reveals several interesting patterns of information consumption on Twitter. First, we find that people not only consume information by following others explicitly but also by listing and subscribing to lists and that the people they list or subscribe to are not the same as the ones they follow. Second, we find that listing and following are more similar to each other than listing and subscribing or subscribing and following. Using temporal analysis, we find that initially, people prefer to use following as a form of information consumption while subscription is a more volatile form of information consumption than following or listing

A Study of Ion-Solvent Interaction in Propylene Carbonate from Viscosity Data

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Constipation

Constipation is a common gastrointestinal (GI) disorder among all age groups. Constipation can be functional or pathological comprising of many etiologies. It can also be classified as acute or chronic; mild to severe. Although in most of the cases it is benign, symptoms can significantly affect the quality of life and cost-related burden for the patient. However, chances of late diagnosis of constipation are high due to a variety of etiologies and variable presentation. Most of the times, it is a great challenge for the clinician to find out the cause of constipation. Healthy lifestyle, especially keeping regular bowel habit, drinking adequate fluid, and the use of high-fiber diet that reduces the viscosity of stool, minimizes intestinal transit time and decreases the chance of constipation. Early diagnosis and management of other underlying factors are important to give relief to the patient from the undue physical and psychological stress

An Empirical Study of the Effects of Principal Component Analysis on Symbolic Classifiers

Classification is a frequently encountered data mining problem. While symbolic classifiers have high comprehensibility, their language bias may hamper their classification performance. Incorporating new features constructed based on the original features may relax such language bias and lead to performance improvement. Among others, principal component analysis (PCA) has been proposed as a possible method for enhancing the performance of decision trees. However, since PCA is an unsupervised method, the principal components may not represent the ideal projection directions for optimizing the classification performance. Thus, we expect PCA to have varying effects; it may improve classification performance if the projections enhance class differences, but may degrade performance otherwise. We also posit that the effects of PCA are similar on symbolic classifiers, including decision rules, decision trees, and decision tables. In this paper, we empirically evaluate the effects of PCA on symbolic classifiers and discuss the findings