Search Results for author:
Found 8 papers, 3 papers with code
Categorical data clustering: 25 years beyond K-modes
The clustering of categorical data is a common and important task in computer science, offering profound implications across a spectrum of applications.
$\mathcal{G}^2Pxy$: Generative Open-Set Node Classification on Graphs with Proxy Unknowns
Node classification is the task of predicting the labels of unlabeled nodes in a graph.
Image-text Retrieval via Preserving Main Semantics of Vision
To address this issue, this paper presents a semantic optimization approach, implemented as a Visual Semantic Loss (VSL), to assist the model in focusing on an image's main content.
Mining compact high utility sequential patterns
To reduce complexity and obtain a compact set of frequent high utility sequential patterns (FHUSPs), this paper proposes an algorithm named CHUSP for mining closed frequent high utility sequential patterns (CHUSPs).
Towards Target High-Utility Itemsets
In this paper, we address this issue by proposing a novel list-based algorithm with pattern matching mechanism, named THUIM (Targeted High-Utility Itemset Mining), which can quickly match high-utility itemsets during the mining process to select the targeted patterns.
Discovering Representative Attribute-stars via Minimum Description Length
However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes. Graphs are a popular data type found in many domains.
Towards Revenue Maximization with Popular and Profitable Products
To fulfill this gap, in this paper, we first propose a general profit-oriented framework to address the problem of revenue maximization based on economic behavior, and compute the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
Enhancing Hyperbolic Graph Embeddings via Contrastive Learning
Inspired by the recently active and emerging self-supervised learning, in this study, we attempt to enhance the representation power of hyperbolic graph models by drawing upon the advantages of contrastive learning.
