A major challenge in document clustering is the extremely high dimensionality. For example, the vocabulary for a document set can easily be thousands of words. On the other hand, each document often contains a small fraction of words in the vocabulary. These features require special handlings. Another requirement is hierarchical clustering where clustered documents can be browsed according to the increasing specificity of topics. In this paper, we propose to use the notion of frequent itemsets, which comes from association rule mining, for document clustering. The intuition of our clustering criterion is that each cluster is identified by some common words, called frequent itemsets, for the documents in the cluster. Frequent itemsets are also used to produce a hierarchical topic tree for clusters. By focusing on frequent items, the dimensionality of the document set is drastically reduced. We show that this method outperforms best existing methods in terms of both clustering accuracy and scalability.

Document clustering techniques mostly rely on single term analysis of the document data set, such as the Vector Space Model. To achieve more accurate document clustering, more informative features including phrases and their weights are particularly important in such scenarios. Document clustering is particularly useful in many applications such as automatic categorization of documents, grouping search engine results, building a taxonomy of documents, and others. This paper presents two key parts of successful document clustering. The first part is a novel phrase-based document index model, the Document Index Graph, which allows for incremental construction of a phrase-based index of the document set with an emphasis on efficiency, rather than relying on single-term indexes only. It provides efficient phrase matching that is used to judge the similarity between documents. The model is flexible in that it could revert to a compact representation of the vector space model if we choose not to index phrases. The second part is an incremental document clustering algorithm based on maximizing the tightness of clusters by carefully watching the pair-wise document similarity distribution inside clusters. The combination of these two components creates an underlying model for robust and accurate document similarity calculation that leads to much improved results in Web document clustering over traditional methods.

We present a practical implementation of the clustering algorithm described in [20]. The clustering algorithm is given either an implicit or explicit representation of the pairwise similarities between n objects and produces a complete hierarchical clustering of the n objects. The implementation runs in O(M log n) time per cluster where M is the number of non-zero entries in the \document-term" matrix, a common implicit representation of similarities between data objects. We perform a thorough experimental evaluation of the algorithm in practice. The results show that the algorithm is better or competitive with existing clustering algorithms (e.g. k-means [21], ROCK [18], p-QR [37]).

searches poses unique challenges. First, we show that one cannot readily import the frequency based feature ranking to cluster the web search results as in the text document clustering. Next, we present TermRank, a variation of the PageRank algorithm based on a relational graph representation of the content of web document collections. TermRank achieves desirable ranking of discriminative terms higher than the ambiguous terms, and ranking ambiguous terms higher than common terms. We experiment with two clustering algorithms to demonstrate the efficacy of TermRank. TermRank is shown to perform substantially better than frequency based classical methods.

The goal of data mining algorithm is to discover useful information embedded in large databases. Frequent itemset mining and sequential pattern mining are two important data mining problems with broad applications. Perhaps the most efficient way to solve these problems sequentially is to apply a pattern-growth algorithm, which is a divide-and-conquer algorithm [9, 10]. In this paper, we present a framework for parallel mining frequent itemsets and sequential patterns based on the divide-and-conquer strategy of pattern growth. Then, we discuss the load balancing problem and introduce a sampling technique, called selective sampling, to address this problem. We implemented parallel versions of both frequent itemsets and sequential pattern mining algorithms following our framework. The experimental results show that our parallel algorithms usually achieve excellent speedups. Categories and Subject Descriptors D.1 [Programming Techniques]: Concurrent programming—parallel programming; H.2.8 [Database Management]:

In this paper, we propose a new term weighting scheme called Term Frequency – Inverse Corpus Frequency (TF-ICF). It does not require term frequency information from other documents within the document collection and thus, it enables us to generate the document vectors of N streaming documents in linear time. In the context of a machine learning application, unsupervised document clustering, we evaluated the effectiveness of the proposed approach in comparison to five widely used term weighting schemes through extensive experimentation. Our results show that TF-ICF can produce document clusters that are of comparable quality as those generated by the widely recognized term weighting schemes and it is significantly faster than those methods. 1.

Frequent itemset mining was initially proposed and has been studied extensively in the context of association rule mining. In recent years, several studies have also extended its application to transaction or document clustering. However, most of the frequent itemset based clustering algorithms need to first mine a large intermediate set of frequent itemsets in order to identify a subset of the most promising ones that can be used for clustering. In this paper, we study how to directly find a subset of high quality frequent itemsets that can be used as a concise summary of the transaction database and to cluster the categorical data. By exploring key properties of the subset of itemsets that we are interested in, we proposed several search space pruning methods and designed an efficient algorithm called SUMMARY. Our empirical results show that SUMMARY runs very fast even when the minimum support is extremely low and scales very well with respect to the database size, and surprisingly, as a pure frequent itemset mining algorithm it is very effective in clustering the categorical data and

The bias of news media is an inherent flaw of the news production process, spanning news gathering, writing, and editing stages. Producer’s subjective valuation, wittingly or unwittingly, takes place during the daily production process. The resulting bias often causes a sharp increase in political polarization and in the cost of conflict on social issues such as Iraq war [7]. With the rapid growth of the Internet news media, it gets very difficult, if not impossible, for readers to have penetrating views on realities against such bias. We present PolyNews, a novel Internet news service framework aiming at mitigating the effect of media bias. PolyNews automatically creates and promptly provides readers with multiple classified viewpoints on a news event of interest. As such, it effectively helps readers understand a fact from a plural of viewpoints and formulate their own, more balanced viewpoints free from specific biased views. The proposed focus-based clustering is realized through two important clustering steps, i.e., news structure-based clustering and collaborative clustering. We the focus of an article. 1.

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The COFI approach for mining frequent itemsets, introduced recently, is an efficient algorithm that was demonstrated to outperform state-of-the-art algorithms on synthetic data. For instance, COFI is not only one order of magnitude faster and requires significantly less memory than the popular FP-Growth, it is also very effective with extremely large datasets, better than any reported algorithm. However, COFI has a significant drawback when mining dense transactional databases which is the case with some real datasets. The algorithm performs poorly in these cases because it ends up generating too many local candidates that are doomed to be infrequent. In this paper, we present a new algorithm COFI* for mining frequent itemsets. This novel algorithm uses the same data structure COFI-tree as its predecessor, but partitions the patterns in such a way to avoid the drawbacks of COFI. Moreover, its approach uses a pseudo-Oracle to pinpoint the maximal itemsets, from which all frequent itemsets are derived and counted, avoiding the generation of candidates fated infrequent. Our implementation tested on real and synthetic data shows that COFI* algorithm outperforms state-of-the-art algorithms, among them COFI itself.