Performance Issues on K-Mean Partitioning Clustering Algorithm

In data mining, cluster analysis is one of challenging field of research. Cluster analysis is called data segmentation. Clustering is process of grouping the data objects such that all objects in same group are similar and object of other group are dissimilar. In literature, many categories of cluster analysis algorithms present. Partitioning methods are one of efficient clustering methods, where data base is partition into groups in iterative relocation procedure. K-means is widely used partition method. In this paper, we presented the k-means algorithm and its mathematical calculations for each step in detailed by taking simple data sets. This will be useful for understanding performance of algorithm. We also executed k-means algorithm with same data set using data mining tool Weka Explorer. The tool displays the final cluster points, but won’t give internal steps. In our paper, we present each step calculations and results. This paper helpful to user, who wants know step by step process. We also discuss performance issues of k-means algorithm for further extension.

Performance Issues on K-Mean Partitioning Clustering Algorithm

In data mining, cluster analysis is one of challenging field of research. Cluster analysis is called data segmentation. Clustering is process of grouping the data objects such that all objects in same group are similar and object of other group are dissimilar. In literature, many categories of cluster analysis algorithms present. Partitioning methods are one of efficient clustering methods, where data base is partition into groups in iterative relocation procedure. K-means is widely used partition method. In this paper, we presented the k-means algorithm and its mathematical calculations for each step in detailed by taking simple data sets. This will be useful for understanding performance of algorithm. We also executed k-means algorithm with same data set using data mining tool Weka Explorer. The tool displays the final cluster points, but won’t give internal steps. In our paper, we present each step calculations and results. This paper helpful to user, who wants know step by step process. We also discuss performance issues of k-means algorithm for further extension.

Empowering marketing management and gaming consumer interaction through AI and citizen science

There has been a significant revolution seen by AI getting incorporated into the management and customer relations of companies. The research of the present Artificial Intelligence (AI) Revolution that influences a variety of fields i.e. video games are the topic of the article. AI systems such as machine learning and data analytics can help brands understand consumer behaviour in much greater detail; hence, companies can better reach and interest potential consumers through personalized marketing plans and campaigns. What is more, this is another case of citizen science projects that can host a large number of artisanal anglers who can together provide data that can make the research wider-reaching. This is when the conclusion is reached, which means, for gaming neither marketing nor game-play is the energy source. The proposed scheme improves the level of customer accuracy and tackles trends timely as well as creates slight space for real-time communication by applying neighbour-based recommendation techniques, neural networks, and sentiment analysis. Its supremacy over the conventional methods of statistical significance is highlighted through the advent of predictive analytics and dynamic pricing approaches. The advantage of deploying natural language processing (NLP) is that it helps to understand what the customers mean with how they write. Measuring the key performance indicators at the end of this approach can be called the method of adaptation and flexibility which makes digital marketing not just refer only the success but also turn to the happiness of customers

Empowering marketing management and gaming consumer interaction through AI and citizen science

There has been a significant revolution seen by AI getting incorporated into the management and customer relations of companies. The research of the present Artificial Intelligence (AI) Revolution that influences a variety of fields i.e. video games are the topic of the article. AI systems such as machine learning and data analytics can help brands understand consumer behaviour in much greater detail; hence, companies can better reach and interest potential consumers through personalized marketing plans and campaigns. What is more, this is another case of citizen science projects that can host a large number of artisanal anglers who can together provide data that can make the research wider-reaching. This is when the conclusion is reached, which means, for gaming neither marketing nor game-play is the energy source. The proposed scheme improves the level of customer accuracy and tackles trends timely as well as creates slight space for real-time communication by applying neighbour-based recommendation techniques, neural networks, and sentiment analysis. Its supremacy over the conventional methods of statistical significance is highlighted through the advent of predictive analytics and dynamic pricing approaches. The advantage of deploying natural language processing (NLP) is that it helps to understand what the customers mean with how they write. Measuring the key performance indicators at the end of this approach can be called the method of adaptation and flexibility which makes digital marketing not just refer only the success but also turn to the happiness of customers