Journal of Data Mining and Management (e-ISSN: 2456-9437)

A Quick Review on Data Mining Techniques

Er. Ankit Sharma — 2017-09-15

In this paper, we studied about the concept of data mining and their techniques. Data mining is a process to extract the pattern in the data from the huge amount of data. For data mining there are some techniques those are apply on the huge amount of data and extract the useful information. In this paper we can study about the categories of these techniques of data mining also to improve their businesses and found excellent results.

An Approach of Association Rule Mining for Control Population Size by using Rough Set Theory

Marziyeh Bahrami — 2017-10-25

Information technology is a turning point in strategic decision-making. In order to achieve resource efficiency, create equilibrium in the workforce of the community and develop macroeconomic policies, population control is must be. Achieving these great goals requires analyzing the information of past years and its uncertainty as well as the lost values. The analysis of past work and ongoing work shows that data mining experts do not address the issue of population control. In this paper, we present a comparative framework for the study of non-recognizable data items, using the theory of rough collections. Our next work in this paper is to evaluate the previous algorithms provided for the preprocessing, feature extraction, and exploration of community rules algorithms (Case Study: population statistics, presented at the Center for Statistics of Iran (ISC)). The results of the experiments show that the proposed method is both useful for strategic decision making in macroeconomic policies, and for the analysis of other demographic data.

Framework for Predictive Analytics with Complex Event Processing

Ajay Acharya — 2017-11-23

Event processing is a technique of tracking and scrutinizing of information about the events that happen and deriving a conclusion from them. Complex Event Processing(CEP) is a technology which allows combining of data from multiple sources and infers complicated patterns from the events. Complex events require real time detection in order to have time for appropriate reactions. However, there are several events which should be prevented rather than responding to them after they have occurred. This can be achieved using Predictive analysis. Predictive analysis(PA) enhances the performance of CEP. In this paper, we define CEP and PA technology and provide a conceptual framework which provides synergy between CEP and PA. This framework can be the basis of general design pattern in future.

Semantic Similarity between Two Documents: A Topic map Approach

Sonam — 2017-12-20

Computing semantic similarity between any two entities (word, sentences, documents) is crucial tasks on the web .Semantic Similarity plays a significant and big role in information retrieval(IR), natural language Processing(NLP) and many other tasks of IR related tasks such as relation extraction, and document clustering. It is a concept where a pair of documents is measured to computing the Semantic Similarity between documents using various similarity measures. Computing similarity between a pair of documents with efficient method is really a major difficult task for the user. Similarity measure those are used to find similarity, assign a real number between 0 and 1 to a pair of documents. If both documents are similar then user will get a numerical value 1 otherwise they will get 0.This paper proposes a framework for computing the semantic similarity between documents based on topic maps. The process starts with pre-processing of the documents using NLP parser. Then Topic map is build that represent the document in compact form and cosine similarity measures is used to measure the similarity between these topic maps.

SENTIMENTAL ANALYSIS OF WHATSAPP DATA USING DATA ANALYTICS TECHNIQUES

C. Premalatha — 2017-12-16

Data, the one, that made a trend and evolution from good old techniques to modern trendy techniques. Based on the source the data is extracted, its form differs that is in recent decades data are scalable, skeptical and diverse compared to old structured data that are derived from specific known sources.The next criterion for the technological evolution is, the storage space that is needed for data. Structured data storage lies in the database whereas for the unstructured and semi-structured ones the storage trend tips to cloud. The vital thing in addition to storage is data handling and analysis, both process when involved in trendy data results in a complexity in terms of containment, processing and visualization of data. To overcome these complexities on trendy unstructured data there emerged a concept called Big Data Analytics. It uses numerous tools and techniques that resolved the problem from data storage to visualization. One such technique is Analytical sandbox, the most central container that stores and handles data in a very appropriate manner. In addition, Big data tools provide a variety of analytical and visualization techniques that produces efficient graphical view of modern data. This paper centered on sentimental analysis of WhatsApp group data in which text mining was incorporated to the chat file and the resulted chat text file is further processed with analytical tools that analyze the chat contents and produces an graphical visualization of the sentiments shared in the group. Its outcome view lies in high echelon of positive opinions compared to other emotions such as anger, fear, disgust, anticipation, joy, sadness, surprise, trust and negative.