# Subspace clustering based local classification using ORCLUS. Assigns clusters and distances and classes for new data according to the intrinsic subspace clusters of an `orclass` classification model. ```r ## S3 method for class 'orclass' predict(object, newdata, type = "nearest", ...) ``` ## Arguments - `object`: Model resulting from a call of `orclass`. - `newdata`: A matrix or data frame to be clustered by the given model. - `type`: Default `"nearest"` computes relative class frequencies of nearest cluster as class posterior probabilities. - ``...``: Currently not used. ## Details For prediction the class distribution of the `"nearest"`" cluster is used. If `type = "fuzzywts"` cluster memberships (see e.g. Bezdek, 1981) are computed based on the cluster distances of cluster assignment by `predict.orclus`. For orclass prediction the class distributions of the clusters are weigthed using the cluster memberships of an observation. ## Returns - **class**: Vector of predicted class levels. - **posterior**: Matrix where coloumns contain class posterior probabilities. - **distances**: A matrix where coloumns are the distances to all cluster centers in the corresponding subspaces for the new data. - **cluster**: The resulting cluster labels for the new data. ## References Aggarwal, C. and Yu, P. (2000): **Finding generalized projected clusters in high dimensional spaces**, Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 70-81. Bezdek, J. (1981): **Pattern recognition with fuzzy objective function algorithms**, Kluwer Academic, Norwell, MA. ## Author(s) Gero Szepannek ## See Also `orclass`, `orclus`, `predict.orclus` ## Examples ```r # definition of a function for parameterized data simulation sim.orclus <- function(k = 3, nk = 100, d = 10, l = 4, sd.cl = 0.05, sd.rest = 1, locshift = 1){ ### input parameters for data generation # k number of clusters # nk observations per cluster # d original dimension of the data # l subspace dimension where the clusters are concentrated # sd.cl (within cluster subspace) standard deviations for data generation # sd.rest standard deviations in the remaining space # locshift parameter of a uniform distribution to sample different cluster means x <- NULL for(i in 1:k){ # cluster centers apts <- locshift*matrix(runif(l*k), ncol = l) # sample points in original space xi.original <- cbind(matrix(rnorm(nk * l, sd = sd.cl), ncol=l) + matrix(rep(apts[i,], nk), ncol = l, byrow = TRUE), matrix(rnorm(nk * (d-l), sd = sd.rest), ncol = (d-l))) # subspace generation sym.mat <- matrix(nrow=d, ncol=d) for(m in 1:d){ for(n in 1:m){ sym.mat[m,n] <- sym.mat[n,m] <- runif(1) } } subspace <- eigen(sym.mat)$vectors # transformation xi.transformed <- xi.original %*% subspace x <- rbind(x, xi.transformed) } clids <- rep(1:k, each = nk) result <- list(x = x, cluster = clids) return(result) } # simulate data of 2 classes where class 1 consists of 2 subclasses simdata <- sim.orclus(k = 3, nk = 200, d = 15, l = 4, sd.cl = 0.05, sd.rest = 1, locshift = 1) x <- simdata$x y <- c(rep(1,400), rep(2,200)) res <- orclass(x, y, k = 3, l = 4, k0 = 15, a = 0.75) prediction <- predict(res, x) # compare results table(prediction$class, y) ```

predict.orclass function