We consider the task of finding frequent parallel episodes in parallel point processes, allowing for imprecise synchrony of the events constituting occurrences (temporal imprecision) as well as incomplete occurrences (selective participation). We tackle this problem with frequent pattern mining based on the CoCoNAD methodology, which is designed to take care of temporal imprecision. To cope wit...

Online mining of closed frequent itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we propose an efficient one-pass algorithm, NewMoment to maintain the set of closed frequent itemsets in data streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the...

the problem of frequent itemset mining is considered in this paper. One new technique proposed to generate frequent patterns in large databases without time-consuming candidate generation. This technique is based on focusing on transaction instead of concentrating on itemset. This algorithm based on take intersection between one transaction and others transaction and the maximum shared items be...

Data mining has been defined as the nontrivial extraction of implicit, previously unknown and potentially useful information from data. Association mining and sequential mining analysis are considered as crucial components of strategic control over a broad variety of disciplines in business, science and engineering. Association mining is one of the important sub-fields in data mining, where rul...

Restricted and repetitive behavior (RRB) is more pervasive, prevalent, frequent, and severe in individuals with autism spectrum disorders (ASDs) than in their typical peers. One subtype of RRB is restricted interests in items or activities, which is evident in the manner in which individuals engage with items (e.g., repetitious wheel spinning), the types of items or activities they select (e.g....

Discovering association rules by identifying relationships among sets of items in a transaction database is an important problem in Data Mining. Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. In this paper, we describe a more efficient algorithm for mining complete frequent itemset...

Frequent item mining, which deals with finding items that occur frequently in a given data stream over a period of time, is one of the heavily studied problems in data stream mining. A generalized version of frequent item mining is the persistent item mining, where a persistent item, unlike a frequent item, does not necessarily occur more frequently compared to other items over a short period o...

OBJECTIVE To use structural equation modeling to provide a preliminary examination of the relationship between parental support and conflict regarding physical activity behaviors in preadolescents with type 1 diabetes. METHOD Parent-child dyads (n = 85, M child age = 10.8) completed physical activity items from the Diabetes Family Behavior Scale, Diabetes Related Conflict Scale, and Self-Care...

Contrast set mining identifies patterns in the data that can best distinguish between groups. Most of the existing work focuses on categorical and batch data, and they do not scale well for large datasets. In this work, we focus on finding contrast patterns for mixed (quantitative and categorical) and streaming data. We adapt a discretization methodology, Supervised Dynamic and Adaptive Discret...