Extracting User-Centric Knowledge on Two Different Spaces: Concepts and Records

Abstract

The growing demand for eliciting useful knowledge from data calls for techniques that can discover insights (in the form of patterns) that users need. Methodologies for describing intrinsic and relevant properties of data through the extraction of useful patterns, however, work on fixed input data, and the data representation, therefore, constrains the discovered insights. In this regard, this paper aims at providing foundations to make the descriptive knowledge that is extracted by pattern mining more user-centric by relying on flexible data structures defined on two different perspectives: concepts and data records. In this sense, items in data can be grouped into abstract terms through subjective hierarchies of concepts, whereas data records can also be organized based on the users' subjective perspective. A series of easy-to-follow toy examples are considered for each of the two perspectives to demonstrate the usefulness and necessity of the proposed foundations in pattern mining. Finally, aiming at experimentally testing whether classical pattern mining algorithms can be adapted to such flexible data structures, the experimental analysis comprises different methodologies, including exhaustive search, random search, and evolutionary approaches. All these approaches are based on well-known and widely recognized techniques to demonstrate the usefulness of the provided foundations for future research works and more efficient and specifically designed algorithms. Obtained insights demonstrate the importance of working with subjectivity: an item is a type of soda but belongs to a pack, including two or more soda types.

Efficient Frequent Chronicle Mining Algorithms: Application to Sleep Disorder

“…Since the early 90s, when the market basket problem was proposed to discover what items were bought together in a transaction, many studies have contributed to an improvement of the efficiency [21] and expressiveness [22] of the proposals. Sequence analysis techniques [11] are some examples of highly expressive proposals required when the sequential order of the items is critical.…”

Identification of Changes in VLE Stakeholders’ Behavior Over Time Using Frequent Patterns Mining

“…Therefore, the pattern is considered a good descriptor of intrinsic and important properties of the data [7]. These patterns should be novel, significant, unexpected, nontrivial and actionable [8].…”

“…Interestingness quality measures of frequent itemsets and association rules should be used to filter, rank and mainly getting more useful results. These measures can be divided into objective or data-driven (statistical and structural properties of data) and subjective or user-driven (user's preferences and goals) [8]. As a result of the related paper review, interestingness, comprehensibility, and usefulness of the found rule or frequent itemset represent the main qualitative characteristics.…”

Exceptional in so Many Ways—Discovering Descriptors That Display Exceptional Behavior on Contrasting Scenarios

“…In general terms, a pattern (itemset) is the key element in any process of eliciting useful knowledge [14] since it defines subsequences or substructures representing any type of homogeneity and regularity in data [7]. Formally, given a set of items I = {i 1 , i 2 , .…”