This is a difficult because: 1. Object appearance varies 2. Way of interacting with object vary among people 3. Both object and body parts of the interest might be occluded Contributions By using motion information alone, we propose an unsupervised framework for clustering and classifying videos of people interacting with objects. The method is based on [2]. We show that: 1. human-object intera...

A challenge involved in applying density-based clustering to categorical datasets is that the ‘cube’ of attribute values has no ordering defined. We propose the HIERDENC algorithm for hierarchical densitybased clustering of categorical data. HIERDENC offers a basis for designing simpler clustering algorithms that balance the tradeoff of accuracy and speed. The characteristics of HIERDENC includ...

While subspace clustering emerged as an application of pattern mining and some of its early advances have probably been inspired by developments in pattern mining, over the years both fields progressed rather independently. In this paper, we identify a number of recent developments in pattern mining that are likely to be applicable to alleviate or solve current problems in subspace clustering a...

The curse of dimensionality turns the high-dimensional data analysis a challenging task for data clustering techniques. In order to deal with highdimensional data, this paper presents a clustering approach that explores the combination of two strategies: semi-supervision and density estimation based on hubness scores. Initial experimental results show a good performance when applied on real dat...

Ant-based clustering is a nature-inspired technique whereas stochastic agents perform the task of clustering high-dimensional data. This paper analyzes the popular technique of Lumer/Faieta. It is shown that the Lumer/Faieta approach is strongly related to Kohonen’s SelfOrganizing Batch Map. A unifying basis is derived in order to assess strengths and weaknesses of both techniques. The behaviou...

Data mining is a process of discovering and exploiting hidden patterns from data. Clustering as an important task of data mining divides the observations into groups (clusters), which is according to the principle that the observations in the same cluster are similar, and the ones from different clusters are dissimilar to each other. Subspace clustering enables clustering in subspaces within a ...

Subspace clustering (also called projected clustering) addresses the problem that different sets of attributes may be relevant for different clusters in high dimensional feature spaces. In this paper, we propose the algorithm DiSH (Detecting Subspace cluster Hierarchies) that improves in the following points over existing approaches: First, DiSH can detect clusters in subspaces of significantly...

We establish the convergence of the fuzzy subspace clustering (FSC) algorithm by applying Zangwill’s convergence theorem. We show that the iteration sequence produced by the FSC algorithm terminates at a point in the solution set S or there is a subsequence converging to a point in S. In addition, we present experimental results that illustrate the convergence properties of the FSC algorithm in...

We propose a transformation method to circumvent the problems with high dimensional data. For each object in the data, we create an itemset of the k-nearest neighbors of that object, not just for one of the dimensions, but for many views of the data. On the resulting collection of sets, we can mine frequent itemsets; that is, sets of points that are frequently seen together in some of the views...

We sincerely thank all the discussants Kjell Doksum and Joan Fujimura (DF); Jianqing Fan (Fan); Peihua Qiu, Kai Yang, and Lu You (QYY); and Yanming Li, Hyokyoung Grace Hong, and Yi Li (LHL) for the thought-provoking and insightful discussions on our paper. We would also like to thank the Editor Fabrizio Ruggeri for processing and organizing the discussion. Ahmed would like to specially thank hi...