We consider the problem of data clustering with unidentified feature quality and when a small amount labelled is provided. An unsupervised sparse method can be employed in order to detect subgroup features necessary for semi-supervised use create constraints enhance solution. In this paper we propose K-Means variant that employs these techniques. show algorithm maintains high performance other ...

One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock exchange market. This research proposes a smart algorithm by means of valuable big data that is generated by stock exchange market and different kinds of methodology to present a smart model.In this paper, we investigate relationships between the data and access to their lat...

Since advanced technologies via social media, internet, virtual communities and networks internet of things (IoT), there are more multi-view data to be collected. Multi-view clustering is a substantial tool as natural design for data. K-means (KM) (single-view) had been extended handling data, called KM (MV-KM). In the literature, most MV-KM algorithms reported influenced by initializations als...

  The clustering problem under the criterion of minimum sum of squares is a non-convex and non-linear program, which possesses many locally optimal values, resulting that its solution often falls into these trap and therefore cannot converge to global optima solution. In this paper, an efficient hybrid optimization algorithm is developed for solving this problem, called Tabu-KM. It gathers the ...

The k-means algorithm is a widely used clustering technique. Here we will examine the performance of multiple implementations of the k-means algorithm in different settings. Our discussion will touch on the implementation of the algorithm in both python and C, and will also mention a 3rd party package for the k-means algorithm that is also written in C but provides python bindings. We will then...

K-means (MacQueen, 1967) [1] is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set to a predefined, say K number of clusters. Determination of K is a difficult job and it is not known that which value of K can partition the objects as per our intuition. To overcome this probl...

One of user's queries from navigation service is to find the nearest facility in terms of time. The facility that is being questioned by the user as a destination may have a queuing service system (e.g. bank), which means that the cost function of the shortest path includes the waiting time at the destination as well as the travel time. This research conducts in the zone 1 of Mashhad with Bank ...