A Model for Trend Analysis in the Online Shopping Scenario Using Multilevel Hesitation Pattern Mining

The present paper proposes a new model for the exploration of hesitated patterns from multiple levels of conceptual hierarchy in the transactional dataset. The usual practice of mining patterns has focused on identifying frequent patterns (i.e., which occur together) in the transactional dataset but uncovers the vital information about the patterns which are almost frequent (but not exactly frequent) called “hesitated patterns.” The proposed model uses the reduced minimum support threshold (contains two values: attractiveness and hesitation) and constant minimum confidence threshold with the top-down progressive deepening approach for generating patterns and utilizing the apriori property. To validate the model, an online purchasing scenario of books through e-commerce-based online shopping platforms such as Amazon has been considered and shown that how the various factors contributed towards building hesitation to purchase a book at the time of purchasing. The present work suggests a novel way for deriving hesitated patterns from multiple levels in the conceptual hierarchy with respect to the target dataset. Moreover, it is observed that the concepts and theories available in the existing related work Lu and Ng (2007) are only focusing on the introductory aspect of vague set theory-based hesitation association rule mining, which is not useful for handling the patterns from multiple levels of granularity, while the proposed model is complete in nature and addresses the very significant and untouched problem of mining “multilevel hesitated patterns” and is certainly useful for exploring the hesitated patterns from multiple levels of granularity based on the considered hesitation status in a transactional dataset. These hesitated patterns can be further utilized by decision makers and business analysts to build the strategy on how to increase the attraction level of such hesitated items (appeared in a particular transaction/set of transactions in a given dataset) to convert their state from hesitated to preferred items.

Discovering Conceptual Hierarchy Through Explicit and Implicit Cues in Child-Directed Speech

In order for children to understand and reason about the world in a mature fashion, they need to learn that conceptual categories are organized in a hierarchical fashion (e.g., a dog is also an animal). The caregiver linguistic input can play an important role in this learning, and previous studies have documented several cues in parental talk that can help children learn a conceptual hierarchy. However, these previous studies used different datasets and methods which made difficult the systematic comparison of these cues and the study of their relative contribution. Here, we use a large-scale corpus of child-directed speech and a classification-based evaluation method which allowed us to investigate, within the same framework, various cues that varied radically in terms of how explicit the information they offer is. We found the most explicit cues to be too sparse or too noisy to support robust learning (though part of the noise may be due to imperfect operationalization). In contrast, the implicit cues offered, overall, a reliable source of information. Our work confirms the utility of caregiver talk for conveying conceptual information. It provides a stepping stone towards a cognitive model that would use this information in a principled way, possibly leading to testable predictions about children's conceptual development.

A literature review: big data and association rule mining

In Today’s modern and advanced era, huge amounts of data have become available on hand to developers and choice makers. Big data successfully handles datasets that are not only large, but also very high in velocity and variety, which difficult to handle using conventional techniques, methods and tools. Multilevel association rule mining plays a very vital role in distributed environment in analysis of big data for preparing different Marketing strategies. As compared to Single Level rule, more precise and prominent information is provided by multilevel association rule and additionally from the hierarchical dataset it generates the conceptual hierarchy of knowledge. This paper aims to analyze Data Mining Technique named Multilevel Association rule, which provides additional important information in comparison to single level rule, and it also invents conceptual hierarchy of information/data from the hierarchical dataset. Tools and techniques of Big Data have also been reviewed in detail.

LIFELONG EDUCATION BETWEEN THE USEFUL AND THE USELESS : LEARNING CITY TOPONYMY

The aim of the paper is to discuss the balance between the useful and the useless in the actual lifelong learning, in a conceptual scheme that goes beyond purely economical or technical evaluations. The authors chose an exemplary field starting from last year’s paper on the exploration of nets. In order to delimitate the research field, the official city toponymy has been considered, both in the structure of hypernyms and in the choices of proper names. The analysis considers Italian city hypernyms, with some regional or local varieties, explaining the underlying historical and contact phenomena. The grouping of similar proper names is then analyzed, finding out segmentations and some strange aversions. The non correspondence between the conceptual hierarchy and the perceptual geographical hierarchy is highlighted. Useful practical reference systems are compared with official ones.

Language effects on the conceptualization of hybrids

abstractThe current study investigates the conceptual hierarchy of humans−animals−plants−non-animate objects by using novel hybrids. Three experiments were conducted. In Experiment 1, twenty-one participants were presented with a grammatically asymmetrical phrase, in which the two components are associated with different linguistic properties, (e.g., a man with a horse’s head) followed by a visual hybrid, and were asked to judge whether the phrase described the hybrid. In Experiment 2, thirty participants were presented with a visual hybrid and were asked to categorize it according to one of its visually presented components in a forced-choice judgment task. In Experiment 3, twenty-nine participants were presented with a visual hybrid that followed a grammatically symmetrical phrase, in which both components carry similar grammatical properties (e.g., half-human half-horse), and were asked to judge whether the phrase described the hybrid. A conceptual hierarchy effect was found in Experiment 1 but not in the other two experiments. These findings show that the hierarchy effect occurs only in verbal tasks that involve asymmetrical grammatical constructions. We suggest that the pragmatic tendency to map the hierarchically higher concept onto the higher grammatical function applies to asymmetrical constructions but not to symmetrical constructions.