SACOC: A spectral-based ACO clustering algorithm

The application of ACO-based algorithms in data mining is growing over the last few years and several supervised and unsupervised learning algorithms have been developed using this bio-inspired approach. Most recent works concerning unsupervised learning have been focused on clustering, where ACO-based techniques have showed a great potential. At the same time, new clustering techniques that seek the continuity of data, specially focused on spectral-based approaches in opposition to classical centroid-based approaches, have attracted an increasing research interest–an area still under study by ACO clustering techniques. This work presents a hybrid spectral-based ACO clustering algorithm inspired by the ACO Clustering (ACOC) algorithm. The proposed approach combines ACOC with the spectral Laplacian to generate a new search space for the algorithm in order to obtain more promising solutions. The new algorithm, called SACOC, has been compared against well-known algorithms (K-means and Spectral Clustering) and with ACOC. The experiments measure the accuracy of the algorithm for both synthetic datasets and real-world datasets extracted from the UCI Machine Learning Repository

Merging KK-means with hierarchical clustering for identifying general-shaped groups

Clustering partitions a dataset such that observations placed together in a group are similar but different from those in other groups. Hierarchical and $K$-means clustering are two approaches but have different strengths and weaknesses. For instance, hierarchical clustering identifies groups in a tree-like structure but suffers from computational complexity in large datasets while $K$-means clustering is efficient but designed to identify homogeneous spherically-shaped clusters. We present a hybrid non-parametric clustering approach that amalgamates the two methods to identify general-shaped clusters and that can be applied to larger datasets. Specifically, we first partition the dataset into spherical groups using $K$-means. We next merge these groups using hierarchical methods with a data-driven distance measure as a stopping criterion. Our proposal has the potential to reveal groups with general shapes and structure in a dataset. We demonstrate good performance on several simulated and real datasets.Comment: 16 pages, 1 table, 9 figures; accepted for publication in Sta

Kernel Spectral Curvature Clustering (KSCC)

Multi-manifold modeling is increasingly used in segmentation and data representation tasks in computer vision and related fields. While the general problem, modeling data by mixtures of manifolds, is very challenging, several approaches exist for modeling data by mixtures of affine subspaces (which is often referred to as hybrid linear modeling). We translate some important instances of multi-manifold modeling to hybrid linear modeling in embedded spaces, without explicitly performing the embedding but applying the kernel trick. The resulting algorithm, Kernel Spectral Curvature Clustering, uses kernels at two levels - both as an implicit embedding method to linearize nonflat manifolds and as a principled method to convert a multiway affinity problem into a spectral clustering one. We demonstrate the effectiveness of the method by comparing it with other state-of-the-art methods on both synthetic data and a real-world problem of segmenting multiple motions from two perspective camera views.Comment: accepted to 2009 ICCV Workshop on Dynamical Visio

A hybrid algorithm for k-medoid clustering of large data sets

In this paper, we propose a novel local search heuristic and then hybridize it with a genetic algorithm for k-medoid clustering of large data sets, which is an NP-hard optimization problem. The local search heuristic selects k-medoids from the data set and tries to efficiently minimize the total dissimilarity within each cluster. In order to deal with the local optimality, the local search heuristic is hybridized with a genetic algorithm and then the Hybrid K-medoid Algorithm (HKA) is proposed. Our experiments show that, compared with previous genetic algorithm based k-medoid clustering approaches - GCA and RAR/sub w/GA, HKA can provide better clustering solutions and do so more efficiently. Experiments use two gene expression data sets, which may involve large noise components

Familial Clustering For Weakly-labeled Android Malware Using Hybrid Representation Learning

IEEE Labeling malware or malware clustering is important for identifying new security threats, triaging and building reference datasets. The state-of-the-art Android malware clustering approaches rely heavily on the raw labels from commercial AntiVirus (AV) vendors, which causes misclustering for a substantial number of weakly-labeled malware due to the inconsistent, incomplete and overly generic labels reported by these closed-source AV engines, whose capabilities vary greatly and whose internal mechanisms are opaque (i.e., intermediate detection results are unavailable for clustering). The raw labels are thus often used as the only important source of information for clustering. To address the limitations of the existing approaches, this paper presents ANDRE, a new ANDroid Hybrid REpresentation Learning approach to clustering weakly-labeled Android malware by preserving heterogeneous information from multiple sources (including the results of static code analysis, the metainformation of an app, and the raw-labels of the AV vendors) to jointly learn a hybrid representation for accurate clustering. The learned representation is then fed into our outlieraware clustering to partition the weakly-labeled malware into known and unknown families. The malware whose malicious behaviours are close to those of the existing families on the network, are further classified using a three-layer Deep Neural Network (DNN). The unknown malware are clustered using a standard density-based clustering algorithm. We have evaluated our approach using 5,416 ground-truth malware from Drebin and 9,000 malware from VIRUSSHARE (uploaded between Mar. 2017 and Feb. 2018), consisting of 3324 weakly-labeled malware. The evaluation shows that ANDRE effectively clusters weaklylabeled malware which cannot be clustered by the state-of-theart approaches, while achieving comparable accuracy with those approaches for clustering ground-truth samples