Macrostate Data Clustering

We develop an effective nonhierarchical data clustering method using an analogy to the dynamic coarse graining of a stochastic system. Analyzing the eigensystem of an interitem transition matrix identifies fuzzy clusters corresponding to the metastable macroscopic states (macrostates) of a diffusive system. A "minimum uncertainty criterion" determines the linear transformation from eigenvectors to cluster-defining window functions. Eigenspectrum gap and cluster certainty conditions identify the proper number of clusters. The physically motivated fuzzy representation and associated uncertainty analysis distinguishes macrostate clustering from spectral partitioning methods. Macrostate data clustering solves a variety of test cases that challenge other methods.Comment: keywords: cluster analysis, clustering, pattern recognition, spectral graph theory, dynamic eigenvectors, machine learning, macrostates, classificatio

Recovered memories, satanic abuse, Dissociative Identity Disorder and false memories in the UK: a survey of Clinical Psychologists and Hypnotherapists

An online survey was conducted to examine psychological therapists’ experiences of, and beliefs about, cases of recovered memory, satanic / ritualistic abuse, Multiple Personality Disorder / Dissociative Identity Disorder, and false memory. Chartered Clinical Psychologists (n=183) and Hypnotherapists (n=119) responded. In terms of their experiences, Chartered Clinical Psychologists reported seeing more cases of satanic / ritualistic abuse compared to Hypnotherapists who, in turn, reported encountering more cases of childhood sexual abuse recovered for the first time in therapy, and more cases of suspected false memory. Chartered Clinical Psychologists were more likely to rate the essential accuracy of reports of satanic / ritualistic abuse as higher than Hypnotherapists. Belief in the accuracy of satanic / ritualistic abuse and Multiple Personality Disorder / Dissociative Identity Disorder reports correlated negatively with the belief that false memories were possible

From graph to hypergraph multiway partition : is the single threshold the only route?

We consider the Hypergraph Multiway Partition problem (Hyper-MP). The input consists of an edge-weighted hypergraph G=(V,ϵ) and k vertices s 1, …, s k called terminals. A multiway partition of the hypergraph is a partition (or labeling) of the vertices of G into k sets A 1, …, A k such that s i  ∈ A i for each i ∈ [k]. The cost of a multiway partition (A 1, …, A k ) is ∑ki=1w(δ(Ai)), where w(δ(⋅)) is the hypergraph cut function. The Hyper-MP problem asks for a multiway partition of minimum cost. Our main result is a 4/3 approximation for the Hyper-MP problem on 3-uniform hypergraphs, which is the first improvement over the (1.5 − 1/k) approximation of [5]. The algorithm combines the single-threshold rounding strategy of Calinescu et al. [3] with the rounding strategy of Kleinberg and Tardos [8], and it parallels the recent algorithm of Buchbinder et al.[2] for the Graph Multiway Cut problem, which is a special case. On the negative side, we show that the KT rounding scheme [8] and the exponential clocks rounding scheme [2] cannot break the (1.5 − 1/k) barrier for arbitrary hypergraphs. We give a family of instances for which both rounding schemes have an approximation ratio bounded from below by Ω(k√), and thus the Graph Multiway Cut rounding schemes may not be sufficient for the Hyper-MP problem when the maximum hyperedge size is large. We remark that these instances have k = Θ(logn)