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Document Clustering: Tips to overcome Challenges

Today, Data is the life-breath to any business. No matter the products and services a business deal with, text analytics solutions enlighten a business for better decision making. Hence, businesses are piled-up with tons of data. Unfortunately, the majority of the data comes unstructured. The abundance of data coming in the form of Free-flowing Text […]

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Today, Data is the life-breath to any business. No matter the products and services a business deal with, text analytics solutions enlighten a business for better decision making. Hence, businesses are piled-up with tons of data. Unfortunately, the majority of the data comes unstructured. The abundance of data coming in the form of Free-flowing Text in the Data Repositories comes as a significant challenge for organizations. However, it holds the potential to benefit a business manifold. IN modern times, organizations deploy various analytical techniques for structuring and processing unstructured data. But, no other techniques come more potential than the Document Clustering methodology. 

Document Clustering is the deployment of the cluster analysis approach on text documents. The process involves Natural Language Processing and Machine Learning. The objective of the process is to comprehend the nature of the unstructured text-based data. It Primarily involves the extraction of the descriptors from textual documents. Consequently, the data gets analyzed to explore the frequency of the data source. Ultimately, the descriptor clusters get identified before the data gets auto-tagged. 

How Document Clustering benefits a business?

Businesses opt for Document Clustering for the following reasons: 

  • The most crucial benefit of Document Clustering is that it enhances the available resources. In case one server in the mechanism fails, the other server will take up the workload. It ensures that an organization can escape wasting time and data, in case the server fails. 
  • Document Clustering distributes ongoing projects across different nodes in the specifications user prefers. It comes effective in reducing the overhead as not all the machines across the framework will be compatible to run projects of all types. It allows a business to utilize its resources with higher flexibility. 
  • As the process involves multiple machines, it unleashes the way for higher processing power. 
  • Growing business brings more intricacy and complexity in business reporting. Document Clustering calls for higher scalability of the available resources. 
  • Document Clustering streamlines the process of managing rapidly growing systems and large data sets. 

What are the significant challenges revolving around Data Clustering?

Even if Document Clustering is a highly potent analytical process, it comes with some challenges as well. Here come the key points that will be especially relevant to account in this context: 

  1. The nodes in the document clustering nodes tend to fail when the framework handles an excessive volume of unstructured data. It hampers the overall; outcome and efficiency. Arranging the adequate support in these instances is not a matter of a Childsplay. 
  2. Users experience significant problems to balance the load. 
  3. Especially first-time users find it challenging to evaluate the count of the optimal clusters. It eventually hampers the efficiency of the overall process. 

Your guide to overcoming the challenges associated with Document Clustering

  1. Emphasize on adequate Failover Support Speaking about the possible ways to overcome the usual troubles with Document Clustering, arranging adequate Failover support is one key point. It ensures that the business intelligence system remains functional, even if there are issues with the hardware or the applications involved. Clustering offers failover support in the following ways: In case a node fails to perform the assigned task, another node will automatically take up the task to perform the desired action. Whenever a node fails to perform, the framework tries to connect the Microstrategy. In such instances, users should log-in back to verify the new code to resubmit the job request.
  2. It is critically important to ensure appropriate load balance: Load Balancing aims to bring the perfect equilibrium in the user-session across all the intelligence servers. It prevents the chances of excessive load working on a single machine. It is a crucial strategy in overcoming the issues with Document Clustering, as precise foresight about the count of the request to the server is almost impossible. Usually, the process involves four-stage load balancing.
  3. It is ideal for taking up the Naïve (K-Means) Approach: If users adopt the Partitioning Clustering process, it demands that they should specify the desired count of clusters that they aspire to generate. In that regard, the K-means approach is one of the most common practices in partitioning Clusters. It involves defining clusters that come within the total variations that evaluate if the clusters have minimized to the desired compactness. As users will pre-determine the count of the clusters, it comes especially beneficial in evaluating variable value for K.
  4. It would help if you determined the Optimal cluster count: Various methodologies got proposed to evaluate the cluster results. Clustering Validation is the term employed in designing the procedure to evaluate the clustering algorithm outcomes. There are 30 odd methodologies for exploring the maximum count of clusters. Here come the critical points in that context:
  • The Elbow Methodology: it is probably the most well-known process in determining the optimal cluster count. The process involves calculating the aggregate of the squares for each cluster, and subsequently, it gets graphed.
  • The Gap Statistics: the process involves comparing the aggregate within the cluster variation to get different values for the Naïve (K). The maximum value in the gap statistics will be the count of the optimal clusters. It implies that there will be significant differences between the uniform distribution points, distributed randomly
  • The Silhouette Method: The process aims to calculate an average of the various values for K. The Maximum Silhoutee will be the optimal cluster count, ranging between a range for the values of K. 

5. Reduce the dimension that paves the way to better data visualization: One of the significant reasons to embrace Document Clustering solution is to ensure the best visualization of crucial data. To serve this purpose, you must consider reducing the dimensions to the extent possible. This adjustment ensures that you give the best visualizations of your data. 

The tricks and tips discussed above will enable users to overcome the significant challenges involved in the process. Efficiency in document clustering will enable a business to gain better insights on business standing, powering its growth to the next level of success. 

Author: Muthamilselvan is a passionate Content Marketer and SEO Analyst. He has 5 years of hands on experience in Digital Marketing with IT and Service sectors

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