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In traditional document clustering methods, a document is considered a bag of words. The fact that the words may be semantically relateda crucial information for clusteringis not taken into account. In this paper we describe a new method for generating feature vectors, using the semantic relations between the words in a sentence. The semantic relations are… (More)

- H. Zegeye, M. Robdera, B. Choudhary
- Computers & Mathematics with Applications
- 2011

Throughout this paper, we always assume thatH is a real Hilbert space, whose inner product and norm are denoted by 〈·, ·〉 and ‖ · ‖. The symbols → and ⇀ are denoted by strong convergence and weak convergence, respectively. ωw xn {x : ∃xni ⇀ x} denotes the weak w-limit set of {xn}. Let C be a nonempty closed and convex subset of H and T : C → C a mapping. In… (More)

In this article we introduce the concepts of lacunary Iconvergent sequences. We investigate its different properties like solid, symmetric, convergence free etc.

Encoding a document in a vector is a very crucial step for any vector space model based IR (Information Retrieval) system. It is obvious that the better these vectors are constructed, the better the performance of any application built on top of it. In traditional document representation methods, a document is considered as a bag of words. The fact that the… (More)

Banach contraction principle is a fundamental result in fixed point theory and has been applied and extended in many different directions. In 2002, Branciari [3] obtained a fixed point theorem for a single mapping satisfying an analogue of Banach’s contraction principle for an integral type inequality. Aliouche [2] established a common fixed point theorem… (More)

The intent of this note is to prove some fixed point and common fixed theorems in a Saks spaces by introducing a weaker inequality analogue to Albert and Delabriere [1]. We have also introduced a control functions which is certainly weaker contraction condition available in the literature of Metric Fixed Point Theory and Applications.

In traditional document clustering methods, a document is considered a bag of words. The fact that the words may be semantically relateda crucial information for clusteringis not taken into account. The feature vector representing the document is constructed from the frequency count of document terms. To improve results, weights calculated from techniques… (More)

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