Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization
نویسندگان
چکیده
منابع مشابه
Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization
Plain vanilla K-means clustering is prone to produce unbalanced clusters and suffers from outlier sensitivity. To mitigate both shortcomings, we formulate a joint outlier-detection and clustering problem, which assigns a prescribed number of datapoints to an auxiliary outlier cluster and performs cardinality-constrained K-means clustering on the residual dataset. We cast this problem as a mixed...
متن کاملRepeated Record Ordering for Constrained Size Clustering
One of the main techniques used in data mining is data clustering, which has many applications in computer science, biology, and social sciences. Constrained clustering is a type of clustering in which side information provided by the user is incorporated into current clustering algorithms. One of the well researched constrained clustering algorithms is called microaggregation. In a microaggreg...
متن کاملPartitioning Complex Networks via Size-Constrained Clustering
The most commonly used method to tackle the graph partitioning problem in practice is the multilevel approach. During a coarsening phase, a multilevel graph partitioning algorithm reduces the graph size by iteratively contracting nodes and edges until the graph is small enough to be partitioned by some other algorithm. A partition of the input graph is then constructed by successively transferr...
متن کاملCardinality constrained combinatorial optimization: Complexity and polyhedra
Given a combinatorial optimization problem and a subset N of natural numbers, we obtain a cardinality constrained version of this problem by permitting only those feasible solutions whose cardinalities are elements of N . In this paper we briefly touch on questions that addresses common grounds and differences of the complexity of a combinatorial optimization problem and its cardinality constra...
متن کاملOnline Clustering and Outlier Detection
Clustering and outlier detection are important data mining areas. Online clustering and outlier detection generally work with continuous data streams generated at a rapid rate and have many practical applications, such as network instruction detection and online fraud detection. This chapter first reviews related background of online clustering and outlier detection. Then, an incremental cluste...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Optimization
سال: 2019
ISSN: 1052-6234,1095-7189
DOI: 10.1137/17m1150670