## 1 Introduction  
Nowadays text mining can be used in different practice areas [1] but the most common are: information extraction and retrieval, text classification and clustering, natural language processing, concept extraction, and web mining. Text analysis can be useful and helps to solve problems, such as plagiarism detection, creating effective anti-spam filters [2], finding duplicates in a large number of documents or finding duplicates on the Internet [3]. Some methods focus on keywords from scientific papers' extraction which helps to find the main aim of papers automatically [4]. In an education system, plagiarism detection is a sensitive issue [5]. Plagiarism is one of the common problems because students keep trying to cheat and present writings which they have not created. Usually the main technique to detect similarity between texts is to extract the bag of words from all text datasets. Then the frequency matrix is created, in other words, texts are converted to numerical expressions. In such a technique, the results depend on selected filters when the bag of words is created. Therefore, it is important to select the right filters to get accurate results. Using this technique, we can analyze all texts or just split it into the parts: sentences, paragraphs, pages or n-grams. Depending on the solving task, n-grams could be formed by using character-level or word-level [2,6]. Similarity results can be evaluated using different methods, for example, statistical, estimation of numerical values, or using various clustering methods, such as k-means, Bayesian, artificial neural networks, etc. [7].  
In this paper an approach is proposed to find similarity between texts by integrating not only a numerical estimation but also text clustering and visualization. The text similarity detection is based on text splitting into word-level n-grams and evaluating it using a self-organizing map (SOM) and four numerical measures. The text analysis using a bag of words is not effective because it is difficult to detect how similar two texts are just analyzing the frequency of separate words. Two different people writing some kind of text individually can use similar or almost the same number of words in the text. The bag of words analysis will show that both texts are similar but there is a bigger probability of accidental text match than analyzing a word-level bag of n-grams [8]. In this paper, four specific measures have been used to evaluate texts similarity: cosine, dice, extended Jaccard's, and overlap coefficient. There is significant different evaluations in the literature [9] but these four measures are commonly used in various fields [10; 11]. The other part of the approach is based on the detection of the text similarity of a SOM. The advantage of this method compared with other clustering methods is that we can get a visual representation of all texts in a dataset, cluster, as well as similarities. It helps to make decisions much quicker than analyzing numerical estimation. The main problem of the SOM is that it does not have measures that help to define how similar texts are in the same cell of SOM. Thus, for this reason, it is effective to combine analysis of the texts using SOM and numerical similarity measures. To get accurate results, we extract word-level n-grams of different length from texts and analyze them. It allows us to find the same phrases between different texts. In such a way, instead of a bag of words, we have a bag of n-grams, which characterize all texts. The experimental investigation was made using a corpus of plagiarized short answers dataset.