Breast cancer is the most common cancer in women, and is considered one of the most common causes of death. It increases by an alarming rate globally. Earlier detection and diagnosis could save lives and improve quality of life. In this paper, a new method for breast cancer diagnosis is presented. The proposed method, SVM-Kmeans, combines Kmeans, an unsupervised learning clustering technique, w...

With the rapid explosion of online news articles, predicting userbrowsing behavior using collaborative filtering techniques has gained much attention in the web personalization area. However, common collaborative filtering techniques suffer from low accuracy and performance. This research proposes a new personalized recommendation approach that integrates user and text clustering based on our d...

In this paper, we compare three initialization schemes for the KMEANS clustering algorithm: 1) random initialization (KMEANSRAND), 2) KMEANS++, and 3) KMEANSD++. Both KMEANSRAND and KMEANS++ have a major that the value of k needs to be set by the user of the algorithms. (Kang 2013) recently proposed a novel use of determinantal point processes for sampling the initial centroids for the KMEANS a...

We consider the problem of efficiently executing data clustering queries in a client-server setting. Specifically, we consider an environment in which the entire data set is housed on a server and a client is interested in interactively performing kMeans clustering on different subsets of this data set. Extant solutions to this problem suffer from (a) a significant amount of remote I/O and (b) ...

While focusing on document clustering, this work presents a fuzzy semi-supervised clustering algorithm called fuzzy semi-Kmeans. The fuzzy semi-Kmeans is an extension of K-means clustering model, and it is inspired by an EM algorithm and a Gaussian mixture model. Additionally, the fuzzy semi-Kmeans provides the flexibility to employ different fuzzy membership functions to measure the distance b...

In this paper, we propose a new document clustering method based on the K-means method (kmeans). In our method, we allow only finite candidate vectors to be representative vectors of kmeans. We also propose a method for constructing these candidate vectors using documents that have the same word. We participated in NTCIR-4 WEB Task D (Topic Classification Task) and experimentally compared our m...