Results 51 to 60 of about 2,022,125 (263)
SLEDgeHammer: A Frequent Pattern-Based Cluster Validation Index for Categorical Data
MathematicsCluster validation for categorical data remains a critical challenge in unsupervised learning, where traditional distance-based indices often fail to capture meaningful structures.
Roberto Douglas G. de Aquino +3 more
doaj +1 more source
Mining Frequent Synchronous Patterns based on Item Cover Similarity
International Journal of Computational Intelligence Systems, 2018 In previous work we presented CoCoNAD (Continuous-time Closed Neuron Assembly Detection), a method to find significant synchronous patterns in parallel point processes with the goal to analyze parallel neural spike trains in neurobiology3,9.
Salatiel Ezennaya-Gomez +1 more
doaj +1 more source
GCG: Mining Maximal Complete Graph Patterns from Large Spatial Data
, 2013 Recent research on pattern discovery has progressed from mining frequent patterns and sequences to mining structured patterns, such as trees and graphs. Graphs as general data structure can model complex relations among data with wide applications in web
Al-Naymat, Ghazi
core +1 more source
Phylogenomic analysis reveals extensive phylogenetic mosaicism in the Human GPCR Superfamily [PDF]
, 2007 A novel high throughput phylogenomic analysis (HTP) was applied to the rhodopsin G-protein coupled receptor (GPCR) family. Instances of phylogenetic mosaicism between receptors were found to be frequent, often as instances of correlated mosaicism and ...
Allaby, Robin G., Woodwark, Mathew
core +1 more source
Clinical Insights Into Hypercalcemia of Malignancy in Childhood
Pediatric Blood &Cancer, EarlyView.ABSTRACT Hypercalcemia of malignancy (HCM) is a rare but life‐threatening metabolic emergency in children that occurs in less than 1% of pediatric cancer cases, with a reported incidence ranging from 0.4% to 1.0% across different studies. While it is observed in 10%–20% of adult malignancies, pediatric HCM remains relatively uncommon.
Hüseyin Anıl Korkmaz
wiley +1 more source
Data Organisation for Efficient Pattern Retrieval: Indexing, Storage, and Access Structures
Big Data and Cognitive ComputingThe increasing scale and complexity of data mining outputs, such as frequent itemsets, association rules, sequences, and subgraphs have made efficient pattern retrieval a critical, yet underexplored challenge.
Paraskevas Koukaras, Christos Tjortjis
doaj +1 more source
IIS-Mine: A new efficient method for mining frequent itemsets [PDF]
Maejo International Journal of Science and Technology, 2012 A new approach to mine all frequent itemsets from a transaction database isproposed. The main features of this paper are as follows: (1) the proposed algorithmperforms database scanning only once to construct a data structure called an invertedindex ...
Supatra Sahaphong
doaj
GTRACE-RS: Efficient Graph Sequence Mining using Reverse Search
, 2011 The mining of frequent subgraphs from labeled graph data has been studied extensively. Furthermore, much attention has recently been paid to frequent pattern mining from graph sequences.
Ikuta, Hiroaki +2 more
core +1 more source
Pediatric Blood &Cancer, EarlyView.ABSTRACT Purpose Retinoblastoma (RB) is the most common pediatric ocular cancer, yet population‐based data on survival and risk factors remain limited. This study aimed to describe survival in a large national RB cohort and identify predictors of death and complications.
Samuel Sassine +14 more
wiley +1 more source
KIPS Transactions on Software and Data Engineering, 2016 현존하는 빈발 패턴 마이닝 방법은 대부분 시간 효율성을 목표로 하고, 물리적 메모리 사용에 매우 의존적이다. 하지만 빅데이터 시대가 도래함에 따라 실제 세상의 데이터베이스는 급속도로 증가하고 있으며, 그에 따라 기존의 방법으로 현실적인 거대한 양의 데이터를 마이닝하기에 물리적 메모리 공간이 부족한 실정이다. 이러한 문제를 해결하기 위해, 빈발 패턴 마이닝의 메모리 의존성을 줄이기 위한 보조저장장치 기반의 연구들이 진행되었으나, 메모리 기반의 방법들에 비해 처리 시간이 너무 많이 소비된다는 한계가 있었다.
Jung-Hun Lee, Youn-A Min
openaire +2 more sources